ethnic disparities in prior mental health service use among incarcerated adolescents

Children and Youth Services Review 78 (2017) 23–31

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Children and Youth Services Review journal homepage: www.elsevier.com/locate/childyouth

Racial/ethnic disparities in prior mental health service use among incarcerated adolescents

MARK

Lewis Hyukseung Lee⁎,1, Sara Goodkind1, Jeffrey J. Shook1 School of Social Work, University of Pittsburgh, United States

A R T I C L E I N F O

A B S T R A C T

Keywords: Adolescent metal health Juvenile justice Mental health service utilization Racial disparities

Research on racial and ethnic disparities in mental health and substance abuse service use among incarcerated youth in the U.S. is inconclusive. This cross-sectional study adds to our understanding of racial and ethnic disparities by examining the prior use of mental health and substance abuse services among incarcerated juveniles. Guided by Andersen's behavioral model of health service utilization, a series of logistic regression analyses were conducted on a non-probability sample of 13–19 year-old youth in two residential facilities for juvenile offenders in Western Pennsylvania (N = 181). Black and Hispanic youth were less likely than White youth to have used mental health and substance abuse services, even when controlling for predisposing, enabling, and need factors. Additional analyses revealed that these differences did not hold across all service types, specifically with regards to outpatient service use. Significant differences did exist, however, in the prior use of inpatient mental health and substance abuse services. This suggests that White youth are often funneled into the mental health system, while youth of color enter the justice system. Implications for racial/ethnic disproportionality in service use and justice system involvement are discussed.

1. Introduction Mental health issues among youth are a primary behavioral health concern in the United States (National Center for Children in Poverty [NCCP], 2006; Owens et al., 2002; Zachrisson, Rӧdje, & Mykletun, 2006). The Methodology for Epidemiology of Mental Disorders in Children and Adolescents Study estimates that approximately 21% of young people aged 9 to 17 in the United States have a diagnosable mental health or substance abuse issue (DeRigne, Porterfield, & Metz, 2009; Shaffer, Fisher, Dulcan, & Davies, 1996). NCCP reports that children's mental health problems have been presented as a widespread child health issue, indicating that one in ten youth experiences serious mental health issues (NCCP, 2006). However, numerous studies find that at least 75% of children with mental health issues do not receive adequate treatment or services (Gudino, Lau, Yeh, McCabe, & Hough, 2009; Gudino, Martinez, & Lau, 2012; NCCP, 2006). At the same time, the situation is worse for youth of color, as studies have found that adolescents of color often receive mental health services at lower rates than White adolescents (Dalton, Evans, Cruise, Feinstein, & Kendrick, 2009; Freedenthal, 2007; Garland et al., 2005; Gudino et al., 2009). Studies have found that youth involved in the juvenile justice system have higher rates of mental health and substance abuse issues than youth

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in the general population, as between 66 and 75% of youth in detention facilities could be diagnosed with one or more mental health problems (Abram, Paskar, Washburn, & Teplin, 2008; Kates, Gerber, & Casey, 2014; Shufelt & Cocozza, 2006; Teplin, Abram, McClelland, Dulcan, & Mericle, 2002; Teplin, Abram, McClelland, Washburn, & Pikus, 2005; Wasserman, McReynolds, Lucas, Fisher, & Santos, 2002; Washburn et al., 2008). Similar to youth in the general population, young people with mental health and substance abuse problems often do not receive services either prior to or during their time in the system. For example, one study found that only 34% of detained youth with mental health issues, including anxiety, affective, or disruptive behavior disorders, received services for these issues (Novins, Duclos, Martin, Jewett, & Manson, 1999). In a recent survey of 83 juvenile detention facilities, only 68% of these facilities offered simple mental health services such as counseling services (Pajer, Kelleher, Gupta, Rolls, & Gardner, 2007). In addition, few licensed mental health professionals were involved in care delivery (Pajer et al., 2007). One study identified only 40% of detained youth with serious mental health issues as having received formal mental health services (Teplin et al., 2005). The lack of service provision identified by these studies is important for several reasons. One, there is some evidence that racial differences in mental health and substance use service provision contribute to the

Corresponding author. E-mail addresses: [email protected] (L.H. Lee), [email protected] (S. Goodkind), [email protected] (J.J. Shook). Address: 2117 Cathedral of Learning, 4200 Fifth Avenue, Pittsburgh, PA 15260, USA, +1-412-624-6304.

http://dx.doi.org/10.1016/j.childyouth.2017.04.019 Received 19 May 2016; Received in revised form 22 April 2017; Accepted 25 April 2017 Available online 26 April 2017 0190-7409/ © 2017 Published by Elsevier Ltd.

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detained females needed mental health and substance abuse treatment. Despite the high prevalence of the need for services, juvenile justice involved youth are often less likely to receive services compared to nonjuvenile justice involved youth (Pumariega et al., 1999). Pumariega et al. (1999) compare incarcerated youth in South Carolina with youth in the general population with respect to the level of prior service utilization. They find that incarcerated youth had a lower utilization of lifetime mental health services, including outpatient and acute mental health services, than non-juvenile justice involved youth (Pumariega et al., 1999). In a study of mental health service utilization in a juvenile detention center, Teplin et al. (2005) find that among detained youth, just 15% received treatment in the detention facility and 8% received community-based treatment after the case disposition. Thus, despite the need for services, only a small percentage of youth receive treatment either in the detention center or in the community prior to or after system involvement (Teplin et al., 2005).

overrepresentation of youth of color in the justice system, as White youth receive treatment services in the mental health system and youth of color are funneled to the juvenile justice system (Dalton et al., 2009; Snyder, 2000). Two, the lack of mental health service receipt prior to entry in to the juvenile justice system and while in the system increases the likelihood that unaddressed issues will continue to affect the youth's behavior (Barrett, Katsiyannis, Zhang, & Zhang, 2014; Kerig & Becker, 2014; Pumariega et al., 1999; Spinney et al., 2016; Yampolskaya & Chuang, 2012). Despite their importance, racial disparities in mental health service receipt among incarcerated youth have received relatively little analysis. Further, existing research on racial and ethnic disparities in mental health and substance abuse service use among youth is mixed. There is some evidence of greater mental health service access among White youth involved in the juvenile justice system (Dalton et al., 2009; Garland et al., 2005; Rawal, Romansky, Jenuwine, & Lyons, 2004). Yet, Kates et al. (2014) looked at a sample of youth in the juvenile justice system and reported that Black youth were more likely than White youth to have used mental health services prior to incarceration. While it is not uncommon for studies from different sites and samples to produce distinct results, more research is needed on racial and ethnic discrepancies in mental health and substance use receipt among justice-system involved youth because access to these services can serve to divert youth from the juvenile justice system or address the issues that lead to their involvement (or re-involvement). Using data from a sample of youth offenders (N = 181) in two residential juvenile justice facilities in Western Pennsylvania, this article builds on existing work by examining racial and ethnic differences in the prior use of mental health and substance abuse services among youth in residential placements in the juvenile justice system. We seek to add to the literature on racial/ethnic disparities and to explore variations in use across different types of mental health and substance use services (e.g., inpatient vs. outpatient), consequently expanding what is known about service receipt among juvenile justice involved youth.

2.2. Racial and ethnic disparities in mental health service use among detained and incarcerated youth One factor related to low levels of mental health and substance abuse service receipt is race/ethnicity. A number of studies note distinct rates of mental health service use among Black, Hispanic, and White youth involved in the juvenile justice system (Dalton et al., 2009; Garland et al., 2005; Rawal et al., 2004). One commonality from the literature is the high need for services by youth of color, but their lower rates of service use compared to White adolescents. With respect to this, Dalton et al. (2009) highlight an important practice implication by examining whether Black and White male adolescents aged 12 to 18 have similar rates of referral to mental health services in juvenile justice facilities. They analyzed three data sets from the Massachusetts Youth Screening Inventory-2 (MAYSI-2), Initial Health Care Screening, and Youth Level of Services/Case Management Inventory, in order to measure mental health treatment history, self-reported mental health screening, and youth's level of risk and criminogenic needs in terms of assisting treatment planning, respectively (Dalton et al., 2009). They assessed that White youth were almost four times as likely to be assigned serious mental illness status compared to Black adolescents. White youth reported higher frequencies of prior mental health treatment history than Black youth, indicating that White youth had more access to mental health services. An important implication was that racial bias from service providers may act as a potential barrier that contributes to persistent racial disparities in service access (Dalton et al., 2009). That is, delinquent youth can be channeled into different systems (e.g., juvenile justice vs. mental health systems) based on their diagnoses (e.g. conduct problems vs. emotional disorders), and such diagnoses often stem from racial bias influencing service providers' clinical decisions (Dalton et al., 2009).

2. Literature review 2.1. High rates of mental health illness among detained and incarcerated youth As discussed previously, many adolescents dealing with mental health and substance use issues in the United States have not received services (Garland et al., 2005; Gudino et al., 2012; Katoka, Zhang, & Wells, 2002). There is also evidence to suggest that service receipt among incarcerated youth, despite higher levels of overall need, is lower than among youth in the general population (Kates et al., 2014). Effectively identifying and addressing the mental health and substance abuse needs of all youth can play a key role in reducing the prevalence of mental health issues (Rawal et al., 2004), and, potentially, limit their involvement in the juvenile justice system (Kates et al., 2014; Pumariega et al., 1999). However, there is an ongoing failure to systematically identify and address the mental health and substance abuse issues of incarcerated youth (Herz, 2001; Kates et al., 2014; Pumariega et al., 1999). This is important because the lack of proper access to treatment to deal with issues and behaviors brought on by mental health and substance abuse issues is likely to bring young people into the juvenile justice system (Pumariega et al., 1999; Spinney et al., 2016). This is illustrated by the fact that a large percentage of the youth who enter the juvenile justice system meet the criteria for psychiatric disorders (Abram et al., 2008). For example, Kates et al. (2014) provide empirical evidence that more than three-fourths of their incarcerated youth participants in Northern California have suffered from acute mental health issues. This is consistent with research conducted by Teplin et al. (2002), in which two-thirds of detained males and three-quarters of

2.3. Conceptual framework Studies on racial disparities among delinquent adolescents in the use of prior mental health services have varied findings in terms of the role that race plays in service receipt. One reason for these differences is due to potential confounding factors affecting mental health service receipt (Garland et al., 2005). Rawal et al. (2004) have suggested the need to incorporate various factors, such as socio-economic status and existing mental health needs, in order to identify the effects that race and ethnicity might have on service receipt. Deciding which factors are confounders is complex and using a conceptual framework can aid in accounting for these factors (Pourhoseingholi, Baghestani, & Vahedi, 2012; Victora, Huttly, Fuchs, & Olinto, 1997). In this study, we use a modified version of Andersen's behavioral model of health service utilization (hereafter, Andersen model) to guide our choice of variables.

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By investigating these questions, this study has two primary objectives: 1) to contribute to the sparse literature on prior mental health and substance abuse service receipt among incarcerated adolescents; and, in doing so, 2) to expand understanding of racial and ethnic disparities in the prior receipt of mental health and substance abuse services among incarcerated youth.

Criteria for inclusion in the study at the boys' facility, which housed nearly 500 boys, were being between 13 and 19 years old and having been in the facility between 3 and 12 months at the time of recruitment. In addition to 27 pilot surveys (not included in this analysis because we added additional questions), we sampled approximately 25% of the boys in the facility. We cannot compare them to the other youth in the facility because such data are not available. However, they were sampled based on age and time spent in the facility, and we have no reason to believe that they do not match the general characteristics of youth in the facility, particularly given our high response rates. The girls' facility had approximately 100 beds, and we continued to recruit all girls entering that facility until we achieved our target of 100 girls. > 50% of the youth were referred to the two facilities from probation and another third were committed straight from the juvenile court. The remaining youth reported coming from the child welfare system or through other means. We asked them to provide a short narrative about the types of offenses that brought them to placement. Often, these offense profiles included a variety of offense types, and 49% of the youth reported being there for a person offense, 35% for a property offense, 37% for a drug offense, and 14% for a weapons offense. Approximately 30% of the youth reported that a truancy issue was also part of the reason they were placed in the facilities. The average length of stay at the boys' facility was approximately 7 months, while it was approximately 4.5 months at the girls' facility. Interviews included questions about demographic factors, service receipt during the 12 months before youth came to the facilities, and neighborhood characteristics and self-report mental health screening questions from the Massachusetts Youth Screening Inventory-second version (MAYSI-2) developed by Grisso and Barnum (2006). Interviewers completed an intensive one-day training session and an interview editor was on site as youth were interviewed to minimize interviewer omissions and errors. The total initial sample was 227 (126 from the boys' facility and 101 from the girls' facility). After managing the missing data through a listwise deletion method, the sample size dropped to 214. Finally, we excluded multiracial and youth who identified themselves as being of other racial/ethnic groups because of small sample sizes, resulting in the inclusion of 181 youth in the current analyses. Data was collected in accordance with protocols approved by the Institutional Review Board at the University of Pittsburgh. Facility staff introduced the study to youth. After the facility staff provided the instructions and if the youth expressed interest, a supervisor at the facility provided approval for the youth to participate in the study, and the youth were referred to the research staff. Prior to administering the instrument, the interviewer explained the purpose of the study and received assent from each youth (consent from those 18 and 19 years old).

3. Methods

3.2. Measures

3.1. Participants and procedures

Various measures of mental health and substance abuse service receipt were used as the dependent variables. The primary dependent variable was a dichotomous measure asking respondents whether they used any mental health or substance abuse service (yes/no) in the 12 months prior to being arrested, followed by a checklist of which types of service they utilized. The goal of this measure is to assess mental health and substance abuse services received prior to becoming involved in the system for the current offense(s). Mental health and substance abuse services can be of varying levels of intensity; thus, we examine inpatient and outpatient service receipt separately (Burns, Angold, & Costello, 1992; Staudt, 2003). The structure of substance use treatment is distinct from that of mental health treatment in terms of administration, finance, and regulation (Burnam & Watkins, 2006). This means treatment approaches between substance abuse and mental health often differ (Dalton et al., 2009), even though mental health disorders and

The Andersen model has been used extensively to assess mental health service use among vulnerable populations, including youth (Alexandre, 2008; García, Gilchrist, Vazquez, Leite, & Raymond, 2011). The Andersen model conceptualizes service use as a function of three factors, including predisposition to use services, enabling conditions for securing services, and needs for such services (Andersen, 1995; Andersen & Aday, 1978). Predisposing factors refer to background factors that influence the propensity of individuals to use more or fewer services, such as race/ethnicity, sex, and family situation (Andersen, 1995). Enabling factors are conditions that make mental health service resources more or less available to individuals, including family financial factors (e.g., income, public assistance, etc.) and community characteristics (e.g., geographic region, community risk factors for inadequate access to mental health care, etc.). Need factors include the individual's perceived and/or evaluated health status or illness, such as subjective perception of health status or medical diagnosis (Andersen, 1995). 2.4. Research questions In sum, the high prevalence of mental health problems among incarcerated youth and existence of racial/ethnic disparities in service receipt have alarmed policy makers, social workers, and various mental health service providers. Detained and incarcerated youth have received services at lower rates, even though they exhibit a higher prevalence of mental health issues than youth in the general population. Even when they have access to treatment opportunities, rates of service utilization differ by race and ethnicity. However, results may also fluctuate due to varying levels of attention to potential confounders. Utilizing a conceptual model is useful in determining which factors are relevant to explaining patterns of racial disparities in the use of services. The current study, guided by the Andersen model (1995), aims to address the following questions: 1. To what extent do racial and ethnic disparities exist in the prior utilization of mental health and substance abuse services among incarcerated adolescents, after controlling for predisposing, enabling, and need factors? 2. What are the predictors associated with the prior utilization of mental health services among incarcerated adolescents?

The data for this study (N = 181) were derived from a nonprobability sample of 13–19 year-old youth in two residential facilities (one for boys and one for girls) for juvenile offenders in Western Pennsylvania. Structured one-on-one interviews were conducted to collect the data. Data collection occurred from June 2009 through February 2010. Both of the facilities were private, non-profit institutions serving youth placed in residential placements and offered a variety of services for youth. These facilities were not part of the state training school system and instead contracted with counties to provide residential services for youth. Youth were primarily from Pennsylvania, and, despite the facilities being located in the western part of the state, many of the youth came from the middle and eastern parts of the state. > 90% of the youth invited to participate in the study completed the survey. 25

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needs (Grisso et al., 2012). In terms of internal consistency, previous studies supported the reliability of MAYSI-2, such that individual scales' alpha coefficients were mostly above 0.70, depending on the settings (Ford, Chapman, Pearson, Borum, & Wolpaw, 2008; Grisso et al., 2012). In our study, Cronbach's alpha for the scale was above 0.70 and the mean inter-item of the scale was in the range of 0.15 and 0.50, which were an adequate level. The construct validity of each MAYSI-2 scale was acceptable on the basis of correlations with the Million Adolescent Clinical Inventory (Dalton et al., 2009; Grisso et al., 2012; Millon, 1993). As treatments for substance use differ from those of mental health issues, we removed eight substance use items from the measure to analyze substance use and mental health problems separately. For substance use, eight items that were drawn from the MAYSI-2 for indicating substance use include: 1) Have you done anything you wish you hadn't, when you were drunk or high? 2) Have your parents or friends thought you drink too much? 3) Have you gotten in trouble when you've been high or have been drinking? 4) If yes, is this fighting? 5) Have you used alcohol or drugs to help you feel better? 6) Have you been drunk or high at school? 7) Have you used alcohol and drugs at the same time? 8) Have you been so drunk or high that you couldn't remember what happened? (Cronbach's alpha = 0.80, mean inter-item r = 0.32) The remaining 44 items drawn from the MAYSI-2 were used to assess mental health problems, including depression and anxiety, anger and irritability, suicidal thoughts, somatic complaints, thought disturbances, and traumatic experiences (Cronbach's alpha = 0.89, mean inter-item r = 0.16).

substance use problems (alcohol/drug) often have connections to each other. Consequently, in addition to examining overall service receipt, we separated substance use from mental health services to see if there were different patterns by race/ethnicity. As a result, we utilized nine measures of mental health and/or substance use service receipt to provide a more in-depth analysis of mental health and substance abuse service use. These include: 1) any inpatient or outpatient mental health or substance abuse services, 2) inpatient services (psychiatric hospitals, residential treatment centers, regular hospitals, and inpatient drug treatment centers), 3) outpatient services (psychiatrists not in hospitals or residential treatment centers, emergency rooms of any hospital, pediatricians or other family doctors, outpatient drug treatment centers), 4) any mental health inpatient or outpatient services, 5) any mental health inpatient services, 6) any mental health outpatient services, 7) any substance use treatment, 8) inpatient substance use treatment, and 9) outpatient substance use treatment. By operationalizing mental health and substance use service receipt in these ways, we are able to provide a more extensive and indepth examination of racial and ethnic differences in service receipt than included in most studies. Other relevant factors guided by the conceptual framework were measured in the survey as follows. Race/ethnicity as an independent variable consisted of three mutually exclusive categories Black, White, and Hispanic, as measured by youth self-report. Guided by the Andersen model, predisposing factors included sex (male = 1) and whether adolescents experienced prior out-of-home placement, including foster care, kinship care, group home or residential treatment facility, and other juvenile justice facility except detention (yes = 1). Enabling factors representing resources and characteristics of family and community included types of residential areas (urban = 1, compared with suburban and rural), public assistance that was dichotomized to indicate whether or not the respondent's family received public assistance such as Section 8 housing, food stamps, access card, or TANF payouts (yes = 1), and a neighborhood disorder scale. The neighborhood disorder scale, ranging from 8 to 24, was created by adding scores from eight items relevant to negative neighborhood conditions, such as vandalism, drug addicts and dealers, traffic, abandoned homes, burglaries and theft, run-down buildings, and assaults and muggings (no problem = 1, some problem = 2, and big problem = 3). A score of 8 meant respondents did not perceive neighborhood disorder at all, a score of 16 meant they perceived some problems, and a score of 24 meant they perceived a lot of problems (Cronbach's alpha = 0.85, mean inter-item r = 0.41). Need factors included substance use and other mental health problems. In order to identify substance use and mental health problems that would signal a need for treatment, we utilized the MAYSI-2 comprising 52 yes/no items (yes = 1). As a mental health screening tool, the MAYSI-2 is widely used for routine administration at entry to juvenile justice facility or service, with the purpose of the identification of youth at risk for suicide and urgent mental health and substance use

3.3. Analysis plan To compare various types of mental health and substance abuse service receipt by race and ethnicity, we utilized chi-square and oneway analysis of variance (ANOVA). For multivariate analysis, a series of logistic regression analyses were employed to examine racial/ethnic differences in the receipt of different types of mental health and substance abuse services while simultaneously controlling for the several types of confounding factors (predisposing, enabling, and need). 4. Results Table 1 exhibits the descriptive statistics for youth in the sample, including race/ethnicity, sex, type of residential area, whether they experienced out-of-home placement, whether their family received public assistance, neighborhood disorder scores, alcohol and drug use scores, and mental health scores. As shown in the table, the majority of youth in the sample were Black (56.9%), followed by White (27.1%) and Hispanic (16.0%). As evident in the table, most of them were from urban areas (70.2%). Slightly less than half of youth has previously been in out-of-home placement (43.6%). Over half – 55.2% – of the youth responded that their families received public assistance. The overall mean neighborhood disorder was 15.73 (SD = 4.16), indicating that on average incarcerated youth perceived there were some pro-

Table 1 Overall sample characteristics (N = 181). Race

Sex

Out of home placement

Type of residential area

Black (n = 103)

White (n = 49)

Hispanic (n = 29)

Male (n = 101)

Female (n = 80)

Yes (n = 79)

No (n = 102)

Urban (n = 127)

Non-urban (n = 54)

56.9%

27.1%

16%

55.8%

44.2%

43.6%

56.4%

70.2%

29.8%

Receiving public assistance

Neighborhood disorder

MAYSI-2: alcohol & drug use

MAYSI-2: mental health

Yes (n = 100)

No (n = 81)

Mean

SD

Mean

SD

Mean

SD

55.2%

44.8%

15.726

4.158

3.661

2.452

17.156

8.069

26

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Table 2 Post hoc results for MAYSI-2 scores (need factor) by race/ethnicity among incarcerated juveniles. Race (N = 180)

M (SE)

SD

Mean difference Black

Need factors: MAYSI-2 alcohol and drug Black (n = 102) 3.137 (0.242) White (n = 49) 4.837 (0.326) Hispanic (n = 29) 3.517 (0.396)

(range 0–8) 2.442 – 2.285 1.699⁎⁎⁎ 2.132 0.389

Need factors: MAYSI-2 mental health (range 0–44) Black (n = 102) 15.677 (0.810) 8.181 – White (n = 49) 19.286 (1.045) 7.312 3.609⁎ Hispanic (n = 29) 18.759 (1.493) 8.039 3.082 ⁎

Table 3 Mental health and substance abuse service receipt, predisposing, and enabling factors by race/ethnicity among incarcerated juveniles (n = 181).

White

χ2

% Hispanic

– − 1.319⁎

–

– − 0.527

–

Services Any services Any inpatient services Any outpatient services Mental health inpatient or outpatient Mental health inpatient services Mental health outpatient services Any substance use treatment Inpatient substance use treatment Outpatient substance use treatment Predisposing factors Sex Male Female Out-of-home placement Enabling factors Type of residence Urban Non-urban Receiving public assistance

p < 0.05. p < 0.001.

⁎⁎⁎

blems in their neighborhoods. For eight items of substance use from the MAYSI-2, when a youth scored 4 or greater (the Caution cut-off score) they were likely to have possible clinical significance. The overall mean alcohol and drug use was 3.66 (SD = 2.45), indicating that on average incarcerated youth in our samples were not in the clinically significant range on the scale. For remaining 44 items of mental health problems from the MAYSI-2, the overall mean mental health score was 17.16 (SD = 8.07). Table 2 presents racial/ethnic comparisons of neighborhood disorder as an enabling factor, and the MAYSI-2 scores as indicators of service need for mental health and substance abuse problems. As evident in the table, there was no significant difference in neighborhood disorder scores across race as determined by the one-way ANOVA (F(2, 176) = 1.73, p > 0.05). For alcohol and drug problems, there was a significant difference by race/ethnicity, F(2, 177) = 8.698, p < 0.001). White youth (M = 4.84, SD = 2.29) had significantly higher scores, compared to Black (M = 3.14, SD = 2.44) and Hispanic youth (M = 3.52, SD = 2.13). White youth, on average, scored greater than the cut-off score which can be inferred as having possible clinical significance. However, there was no significant difference between Black and Hispanic youth. Regarding mental health problems, there was a significant difference by race/ethnicity, F(2, 177) = 4.13, p < 0.05). White youth (M = 19.29, SD = 7.31) had significantly higher scores than Black youth (M = 15.68, SD = 8.18). There was no significant difference both between White and Hispanic (M = 18.76, SD = 8.04) and between Black and Hispanic youth. Table 3 displays the frequency of prior mental health service receipt, other predisposing and enabling factors by race/ethnicity. According to bivariate statistics, White youth were more likely to have received any type of inpatient services (White: 69.4% vs. Black: 32.0%, Hispanic: 37.9%, χ2 = 19.26, p < 0.001), inpatient mental health services (White: 57.1% vs. Black: 30.1%, Hispanic: 37.9%, χ2 = 10.25, p < 0.01), and inpatient substance use treatment (White: 34.7% vs. Black: 2.9%, Hispanic: 0%, χ2 = 38.41, p < 0.001) when compared to Black and Hispanic youth. There were no significant racial/ethnic differences in receipt of outpatient services (χ2 = 1.27, p > 0.05), outpatient mental health services (χ2 = 1.37, p > 0.05), and outpatient substance use treatment (χ2 = 5.05, p < 0.10). In addition, most Black (85.4%) and Hispanic (75.9%) youth resided in urban areas, while the majority of White (65.3%) youth lived in nonurban areas (χ2 = 41.38, p < 0.001). Tables 4, 5, and 6 depict the results of logistic regression models (N = 181 in all models) that examined racial/ethnic differences in service receipt while controlling for predisposing, enabling, and need factors. When controlling for all predisposing, enabling, and need factors, the model indicated that Black and Hispanic youth were less likely than White youth to use any type of services (OR = 0.14, p < 0.01, OR = 0.09, p < 0.01, respectively), inpatient services

Black

White

Hispanic

69.9 32.0 60.2 64.1 30.1 54.4 18.4 2.9 16.5

91.8 69.4 67.3 85.7 57.1 63.3 40.8 34.7 26.5

69.0 37.9 55.2 65.5 37.9 51.7 6.9 0 6.9

57.3 42.7 38.8

51.0 49.0 46.9

58.6 41.4 55.2

85.4 14.6 58.3

34.7 65.3 44.9

75.9 24.1 62.1

9.530⁎⁎ 19.263⁎⁎⁎ 1.268 7.782⁎ 10.249⁎⁎ 1.369 14.376⁎⁎⁎ 38.412⁎⁎⁎ 5.052† 0.639

2.752 41.377⁎⁎⁎

3.045

†

p < 0.10. p < 0.05. p < 0.01. ⁎⁎⁎ p < 0.001. ⁎

⁎⁎

(OR = 0.22, p < 0.001, OR = 0.21, p < 0.01, respectively), and any inpatient and outpatient mental health service, (OR = 0.25, p < 0.05, OR = 0.17, p < 0.01, respectively). Black youth were also less likely to use inpatient mental health services (OR = 0.37, p < 0.05) and inpatient substance use treatments (OR = 0.05, p < 0.001) than White youth. As for the predisposing factors, girls were more likely to use both inpatient and outpatient substance use treatments than boys (OR = 5.66, p < 0.05, OR = 3.26, p < 0.01, respectively), while girls were less likely to have received any type of mental health treatment (OR = 0.43, p < 0.05). Youth who had not experienced out-of-home placement were less likely to have used any inpatient services, inpatient mental health services, and inpatient substance use treatments than those who had experienced out-of-home placement (OR = 0.39, p < 0.01, OR = 0.47, p < 0.05, OR = 0.16, p < 0.01, respectively). In terms of need factors, incarcerated youth who scored higher on alcohol and drug use problems were more likely to utilize outpatient substance use treatment (OR = 1.29, p < 0.05). Additionally, youth who received higher scores in mental health problems were more likely to use any type of services (OR = 1.09, p < 0.01), inpatient services (OR = 1.08, p < 0.01), any mental health services (OR = 1.11, p < 0.001), inpatient mental health services (OR = 1.09, p < 0.001), and outpatient mental health services (OR = 1.05, p < 0.05). None of the enabling factors (e.g., types of residential areas, welfare status, and neighborhood disorder) were associated with the receipt of mental health and substance abuse services (p > 0.05). 5. Discussion 5.1. Race and prior service use among youth in the juvenile justice system The goal of this paper is to advance our understanding of racial/ ethnic disparities in prior mental health and substance abuse service receipt among incarcerated youth. We find that there are significant racial/ethnic disparities in prior mental health service use among incarcerated youth. These racial/ethnic differences held after adjusting 27

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Table 4 Multivariate logistic regression analyses of factors associated with mental health service use among incarcerated youth (N = 181). All (N = 181)

Race (ref. = White) Black Hispanic Predisposing factors Female No out-of-home placement Enabling factors Area of residence (ref. urban) Receiving public assistance (ref. yes) Neighborhood Need factors MAYSI-2 (alcohol and drug) MAYSI-2 (mental health)

M1: Any inpatient & outpatient services OR (95% CI)

M2: Any inpatient services OR (95% CI)

M3: Any outpatient services OR (95% CI)

0.14 (0.04–0.53)⁎⁎ 0.09 (0.02–0.42)⁎⁎

0.22 (0.09–0.56)⁎⁎⁎ 0.21 (0.07–0.65)⁎⁎

0.68 (0.28–1.62) 0.50 (0.17–1.42)

0.47 (0.21–1.06)† 0.59 (0.27–1.32)

0.92 (0.45–1.87) 0.39 (0.20–0.77)⁎⁎

0.76 (0.39–1.47) 1.21 (0.64–2.29)

0.38 (0.14–1.02)† 0.98 (0.45–2.14) 1.00 (0.90–1.10)

0.89 (0.37–2.10) 1.17 (0.58–2.34) 0.97 (0.89–1.07)

0.61 (0.27–1.36) 1.01 (0.53–1.93) 1.01 (0.92–1.10)

0.97 (0.80–1.17) 1.09 (1.02–1.16)⁎⁎

0.94 (0.80–1.10) 1.08 (1.03–1.14)⁎⁎

1.03 (0.89–1.20) 1.04 (0.99–1.09)

Nine separate models are presented in Tables 4 through 6. Model 1 through Model 3 are presented in Table 4. † p < 0.10. ⁎⁎ p < 0.01. ⁎⁎⁎ p < 0.001.

Dare, & Seck, 2011; Rawal et al., 2004; Ryan, Herz, Hernandez, & Marshall, 2007), existing racial bias may cause youth of color to experience more justice system involvement through more arrests and placement in detention facilities than White youth. This happens disproportionately more in drug-related offenses (Welty et al., 2016). Even though Black youth are less likely to be involved in drug-related offenses than White youth, Black youth are more likely to be arrested for them (Welty et al., 2016). Adding to this, case processing often depends on the racial stereotypes of decision makers in the juvenile justice system, which may also funnel youth into different systems (Dalton et al., 2009; Rawal et al., 2004). Racial bias may affect perceptions of the actions of youth being viewed as criminal versus stemming from emotional issues (Rawal et al., 2004). That is, even when youth are involved in the same types of offenses, Black youth may be more likely to be viewed as delinquent, which then leads to more severe consequences such as incarceration in the juvenile justice system (Bridges & Steen, 1998), while White youth may be more likely seen as acting from internal conflicts caused by a

for relevant factors guided by the Anderson conceptual model for service utilization. This was consistent with previous studies with samples of youths in the justice system, in which White youth were more likely to have received treatment prior to incarceration than youth of color (Abram et al., 2008; Dalton et al., 2009; Garland et al., 2005; Rawal et al., 2004). Although youth of color were less likely to have utilized mental health and substance abuse services overall, race/ ethnicity was only significantly related to prior service use for inpatient-related care (e.g., inpatient services, inpatient mental health services, and inpatient substance use treatment), and was not significantly related to the use of service for outpatient-related care, when examined separately. Thus, White youth may avoid the juvenile justice system by getting funneled into the mental health system, while youth of color are more likely to enter the justice system for similar issues. One explanation may be the racial bias of key decision makers in the justice system (Rawal et al., 2004). As many have discussed (e.g., Leiber & Fox, 2005; Mallett, Stoddard-

Table 5 Multivariate logistic regression analyses of factors associated with mental health service use among incarcerated juveniles (N = 181). All (N = 181)

Race (ref. = White) Black Hispanic Predisposing factors Female No out-of-home placement Enabling factors Area of residence (ref. urban) Receiving public assistance (ref. yes) Neighborhood Need factors MAYSI-2 (alcohol And drug) MAYSI-2 (mental health)

M4: Any MH inpatient & outpatient OR (95% CI)

M5: MH inpatient services OR (95% CI)

M6: MH outpatient services OR (95% CI)

0.25 (0.08–0.76)⁎ 0.17 (0.05–0.65)⁎⁎

0.37 (0.15–0.88)⁎ 0.40 (0.14–1.15)†

0.74 (0.32–1.72) 0.56 (0.20–1.55)

0.43 (0.20–0.93)⁎ 0.51 (0.24–1.07)†

0.68 (0.34–1.37) 0.47 (0.24–0.92)⁎

0.65 (0.34–1.24) 0.94 (0.51–1.76)

0.50 (0.19–1.27) 0.92 (0.44–1.91) 0.96 (0.88–1.06)

0.90 (0.39–2.07) 1.44 (0.73–2.87) 0.96 (0.88–1.05)

0.84 (0.38–1.86) 0.89 (0.47–1.69) 0.99 (0.91–1.07)

0.95 (0.79–1.13) 1.11 (1.04–1.17)⁎⁎⁎

0.91 (0.78–1.07) 1.09 (1.04–1.15)⁎⁎⁎

0.99 (0.85–1.14) 1.05 (1.00–1.10)⁎

Nine separate models are presented in Tables 4 through 6. Model 4 through Model 6 are presented in Table 5. † p < 0.10. ⁎ p < 0.05. ⁎⁎ p < 0.01. ⁎⁎⁎ p < 0.001.

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Table 6 Multivariate logistic regression analyses of factors associated with Mental health service use among incarcerated juveniles (N = 181). All (N = 181)

Race (ref. = White) Black Hispanic Predisposing factors Female No out-of-home placement Enabling factor Area of residence (ref. urban) Receiving public assistance (ref. yes) Neighborhood Need factors MAYSI-2 (alcohol and drug) MAYSI-2 (mental health)

M7: Any substance use treatment OR (95% CI)

M8: Inpatient substance use treatment OR (95% CI)

M9: Outpatient substance use treatment OR (95% CI)

0.32 (0.12–0.85)⁎ 0.11 (0.02–0.54)⁎⁎

0.05 (0.01–0.25)⁎⁎⁎

0.57 (0.20–1.63) 0.23 (0.04–1.24)†

2.93 (1.30–6.60)⁎⁎ 1.12 (0.51–2.44)

5.66 (1.42–22.55)⁎ 0.16 (0.04–0.61)⁎⁎

3.26 (1.32–8.05)⁎⁎ 1.56 (0.66–3.69)

0.50 (0.18–1.39) 0.96 (0.43–2.16) 0.95 (0.85–1.07)

0.72 (0.15–3.41) 0.83 (0.22–3.13) 1.01 (0.83–1.24)

0.39 (0.12–1.21) 1.20 (0.50–2.87) 0.93 (0.83–1.06)

1.19 (0.99–1.44)† 1.00 (0.94–1.06)

1.12 (0.83–1.50) 0.99 (0.90–1.10)

1.29 (1.04–1.61)⁎ 0.99 (0.93–1.06)

Nine separate models are presented in Tables 4 through 6. Model 7 through Model 9 are presented in this table. † p < 0.10. ⁎ p < 0.05. ⁎⁎ p < 0.01. ⁎⁎⁎ p < 0.001.

racial disparities in the prevalence of substance use disorders (Welty et al., 2016) reinforces our finding. Welty et al. (2016) examined 1829 delinquent adolescents in Chicago and discovered that White youth used illegal substances, including hard drugs, more often than Black youth. For example, White youth had 32.1 times the odds of hard drug (e.g., cocaine) use disorder (Welty et al., 2016). Overall, our MAYSI-2 scores support the high occurrence of mental health and substance use problems among White youth in the juvenile justice system.

mental illness that may propel them into the mental health system, rather than the justice system (Leiber & Fox, 2005; Mallett et al., 2011; Rawal et al., 2004). Thus, racial bias may explain the higher prior inpatient service receipt among White youth in our study, who might be able to avoid the justice system more often than youth of color. However, the pathways to different systems based on racial bias still remain difficult to assess, given that some argue this may not be attributed to racial bias, but driven by genuine differences in the prevalence of mental health or substance use problems between White youth and youth of color (Cauffman, 2004), as noted in this paper and elsewhere (e.g., Vaughn, Wallace, Davis, Fernandes, & Howard, 2008; Welty et al., 2016). Therefore, further examination is needed to determine whether racial disparities in both incarceration and service receipt are caused by systematic racial bias of decision makers, real differences in the prevalence of mental illness among delinquent youth, or some combination of the two.

5.3. Out-of-home placement and prior service use Our finding suggests child welfare agencies or foster care homes affiliated with out-of-home placement tend to potentially serve as gateways to mental health providers (Glisson & Green, 2006; James, Landsverk, Slymen, & Leslie, 2004). Consistent with other research (James et al., 2004; McMillen et al., 2004), our analysis revealed youth who had experienced out-of-home placement (e.g., foster care, kinship care, a group home, residential treatment facility, other juvenile justice facility, etc.) were more likely than those without such experiences to have used mental health services (e.g., inpatient services, inpatient mental health services, and inpatient substance use treatments). Scholars point out general causes of removal from biological parents include histories of abuse and neglect, poverty, family dysfunction, and parental substance abuse (James et al., 2004), indicating a great need for mental health service among youth in out-of-home placement (Clark, Yampolskaya, & Robst, 2011; Glisson & Green, 2006; Yampolskaya & Chuang, 2012). Thus, youth in out-of-home placement may display indicators of need during their outof-home placement period, triggering their exposure to the mental health system. However, we could not determine the causal association between such need and involvement in the use of inpatient related mental health and substance abuse services among our sample.

5.2. Prevalence of mental health and substance use problems among incarcerated youth Like others (Cauffman, 2004; Dalton et al., 2009; Vaughn et al., 2008; Welty et al., 2016), this study extends the literature on the prevalence of mental health and substance use problems among incarcerated youth. In order to identify the levels of need among incarcerated youth, we analyzed mental health scores drawn from the MAYSI-2. The results revealed that White youth had more mental health problems than Black youth. Regarding the multivariate analyses that controlled for the MAYSI-2 mental health scores and other relevant factors, youth with higher mental health scores were more likely to use any service overall, any inpatient services, mental health services overall, inpatient mental health services, and outpatient mental health services. For substance use problems analyzed using items drawn from the MAYSI-2, we also found that White youth displayed higher average scores with possible clinical significance than youth of color. In multivariate analysis, higher scores for alcohol and substance use were significant predictors of receipt of outpatient substance use treatment. The higher average substance use scores among White youth imply substance use problems are more common among White youth than youth of color. Related to this, a recent 12-year longitudinal study on

5.4. Limitations There are several limitations to the current study. First, it utilized a cross-sectional design, which limits our ability to examine causation. Future studies need to adopt a longitudinal design to examine whether race and ethnicity affect the likelihood of mental health service use. Second, there might be an issue of generalizability, since we focused on detained youth from Western Pennsylvania (though it should be noted 29

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Acknowledgements

that many of the youth in these facilities came from Eastern Pennsylvania). Third, some of the subgroups' sample sizes (e.g., 49 Whites and 29 Hispanics) were relatively small, which affected the power of our analyses. Fourth, enabling factors were not fully accounted for due to data limitations. Related to enabling factors, the oversimplification of receiving public assistance (e.g., Section 8 housing, food stamps, access card, or TANF) can be another limitation. It is important to take into account whether receiving public assistance can be a barrier or facilitator to accessing prior mental health services or whether poverty levels among delinquent youth have an impact on prior service access. In addition to this, other relevant SES variables, such as types of insurance and income levels, which are important enabling factors in the Andersen model, but have not been controlled for, must be included for a more accurate explanation. Finally, our study does not incorporate a critical predictor of the use of mental health services, namely specific type of problems. Particularly important may be whether youth are diagnosed as having internalizing and/or externalizing problems. The former refers to symptoms associated with major depression, general anxiety or traumatic stress. The latter includes symptoms related to conduct disorder, oppositional defiant disorder, or attention deficit disorder (Chisolm, Mulatu, & Brown, 2009; Gudino et al., 2009). It is recommended that future studies examine service use among incarcerated youth by considering types of mental health problems and whether they are diagnosed differently based on race/ethnicity.

This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors. References Abram, K. M., Paskar, L. D., Washburn, J. J., & Teplin, L. A. (2008). Perceived barriers to mental health services among youth in detention. Journal of American Academy of Child and Adolescent Psychiatry, 47(3), 301–308. Alexandre, P. K. (2008). Mental health care for youth: Predictors of use are not always the same as predictors of volume. The Social Science Journal, 45(4), 619–632. Andersen, R. M. (1995). Revisiting the behavioral model and access to medical care: Does it matter? Journal of Health and Social Behavior, 36(1), 1–10. Andersen, R. M., & Aday, L. A. (1978). Access to medical care in the U.S.: Realized and potential. Medical Care, 16, 533–546. Barrett, D. E., Katsiyannis, A., Zhang, D., & Zhang, D. (2014). 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6. Conclusion Despite these limitations, our results have both policy and practice implications. Potential policy remedies to address racial disparities in mental health and substance use treatment among detained youth may be tied to juvenile justice reform. In current reform efforts to reduce racial disparities in the juvenile justice system (National Juvenile Justice Network [NJJN], 2014), major goals include preventing youth of color from entering the juvenile justice system and reducing racial bias at each decision-making point (Shoenberg, 2012). We found significant racial differences in the prior receipt of inpatient services, suggesting that perhaps White youth are more likely to be funneled into mental health treatment while the same behaviors send youth of color into the justice systems. Recommendations for policy and practice include increasing cultural competence among juvenile justice officials and service providers (Dalton et al., 2009; Bridges & Steen, 1998; NJJN, 2014), implementing standardized screening tools (Shoenberg, 2012), and expanding collaboration among stakeholders (Shoenberg, 2012). Enhancing cultural competence among justice officials and service providers helps them reduce racial bias and overcome inequity (Dalton et al., 2009). Using objective screening instruments allows decision makers to collect accurate data of youth's clinical conditions of mental health and substance abuse (Shoenberg, 2012). Lastly, the first two recommendations should be worked on collaboratively by stakeholders who are most directly impacted by the issue, such as systeminvolved youth and their families, advocates, law enforcement, schools, faith-based leaders, members of community organizations, and mental health service providers (Shoenberg, 2012). Ultimately, future research should continue to focus on the intersection of the juvenile justice and mental health systems to establish a more precise understanding of racial/ethnic disparities in both systems. Further, continuous assessment could help to address unmet mental health needs, especially among youth of color in the juvenile justice system, by considering multiple factors, including racial bias among decision-makers, real differences in the prevalence of mental health problems among delinquent adolescents, and relationships among race/ ethnicity, poverty, and mental health problems.

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