Abstract
Mental health courts (MHCs) are diversion programs for offenders with mental illness. Research has demonstrated that MHC participants receive more treatment than traditional court participants. However, little is known about racial/ethnic disparities in community treatment utilization among MHC participants compared with traditional court participants. The present study aimed to fill this gap. Data are from the MacArthur MHC Project which includes objective and subjective information from four MHC samples with traditional court samples at each site. Within the traditional court sample, African Americans were less likely than Whites to receive mental health and substance abuse services. However, significant racial/ethnic disparities were not found for the MHC sample. In an interaction model, African Americans were still less likely to use substance abuse services (but not mental health services) compared with the Whites. However, African American MHC participants utilized more substance abuse services than their counterparts. Policy and practice implications are discussed.
Keywords
Similar to other complex systems, the criminal justice system is ever-evolving. Change often comes about in response to perceived and actual problems. For about the past 50 years, one of the largest problems the criminal justice system has had to contend with has been an influx of offenders with mental health problems (Lamb & Weinberger, 1998; Torrey et al., 2014). The criminal justice system was not designed, nor particularly equipped, to handle this population. One of the more encompassing responses to this problem of the overrepresentation of offenders with mental health problems in our legal system has been the creation of mental health courts (MHCs). MHCs are specialty, problem-solving courts that aim to divert offenders with mental health problems from incarceration (jail and prison) and into community-based treatment (Redlich, Steadman, Monahan, Robbins, & Petrila, 2006). A main premise underlying these courts is that the repeated cycling through the criminal justice system that often plagues offenders with mental illness will be reduced with the advent of community mental health, and often, substance use, treatment (Fisher, Silver, & Wolff, 2006; Slate, 2003).
MHCs began to proliferate in the United States 20 years ago. And, for the most part, MHCs have been found to reduce recidivism in comparison with similar offenders who remained in traditional courts (e.g., Steadman, Redlich, Callahan, Robbins, & Vesselinov, 2011). However, the MHC intervention is not effective for all who receive it. As of late, a focus of MHC research has been to investigate for whom and under what circumstances the courts are effective (e.g., Redlich & Han, 2014; Steadman et al., 2011).
In the present study, a main goal is to examine how racial/ethnic background affects receipt and type of community services in two samples of offenders with mental illness, one processed through a MHC, and the other through the traditional criminal justice system. There is a wealth of research demonstrating that in comparison with Caucasians, minorities are significantly less likely to seek out, and benefit from, mental health and substance use treatment (Alegria, Canino, Vera, Rusch, & Ortega, 2002; Leong & Lau, 2001). Although several studies on MHCs have included or controlled for race/ethnicity in their models, to our knowledge, little research has directly examined how race/ethnicity associates with community treatment in the context of MHCs (but see, Ray & Dollar, 2013). Indeed, there is a dearth of research on service utilization in MHCs. Rather, much of the research has tended to focus on the effect of the MHC intervention on future arrests (although certainly there is research on treatment utilization, which we review below). Thus, another goal of the present research is to more fully investigate the factors (including race/ethnicity and type of court) that influence receipt of community treatment. To accomplish our goals, we mine the rich dataset of the MacArthur MHC project (Steadman et al., 2011), and utilize the Andersen Behavioral Service Utilization Model (Aday & Andersen, 1998) as a guiding framework. This model, initially developed in the late 1960s, focuses on the factors affecting the use of health services (Andersen, 1995). Andersen divided factors associated with the usage of health care service into three dynamics: predisposing, enabling, and need factors. Predisposing factors include demographic characteristics, socioeconomic status, attitude, and beliefs (Wolinsky, 1978). Enabling factors include items such as income, insurance status, and sources of accessibility for care (Jahangir, Irazola, & Rubinstein, 2012). Last, need factors represent the actual need for health care services (Andersen, 1995). Thus, this factor includes health/mental health status, restricted activities during daily life, and symptoms. The Andersen Healthcare Utilization Model has been used for many types of treatment, racial/ethnic groups, and settings over many decades (Andersen, 1995; Anderson, Green, & Payne, 2009; Rivara et al., 2007; Schneeweiss & Avorn, 2005).
Race/Ethnicity and MHCs
Racial/ethnic disparities in the criminal justice system at large are well-known (Hammarström & Janlert, 2002; Thomas, Benzeval, & Stansfeld, 2005). For example, by the age of 23, 49% of African Americans and 44% of Hispanics will have been arrested, in comparison with 38% of Whites (McKee-Ryan, Song, Wanberg, & Kinicki, 2005). Because of these disparities, it is somewhat surprising that researchers have not more fully investigated how race/ethnicity influences referral and acceptance into MHCs, and success in these specialty courts. Some evidence of racial disparities in drug courts has been noted. Specifically, Huddleston, Marlowe, and Casebolt (2008) found that only 21% of drug court participants were African Americans, whereas they made up 44% of the prison population. Similarly, Hispanics comprised only 10% of drug court participants, but approximately 20% of prisons. MHCs and drug courts share several aspects. For example, both are problem-solving courts, aim to divert offenders into community-based treatment from incarceration, and use a “combined systems” approach (see Liu & Redlich, 2014). Often, communities that have established MHCs first experimented with specialty courts by instituting drug courts (Redlich, 2013).
There are multiple points at which racial/ethnic disparities may play out in the MHC process. One initial point is referral into the MHC. Steadman, Redlich, Griffin, Petrila, and Monahan (2005) found that White, older women were more likely to have been referred into these specialty courts. Seven courts were examined and found to vary widely in terms of the proportion of African Americans (the only minority examined) referred; 0% to 62% of referrals were African Americans, which the authors indicated was partly a reflection of the racial makeup of the community in which the court was located. Steadman et al. (2005), however, did not find race (White vs. African American) to influence MHC acceptance decisions. Thus, whereas African Americans (and younger men) in these seven courts were less likely to have been referred into the MHC, once referred, race did not play a role in accepting the person into the court. Trupin and Richards (2003) and Dirks-Linhorst and Linhorst (2012) also did not find race/ethnicity to influence MHC enrollment rates. In contrast, in the San Francisco MHC, McNiel and Binder (2007) found that non-White race/ethnicity associated with a higher likelihood of selection into the court, which again may reflect the community makeup, rather than discriminatory practices.
Race/ethnicity may also influence the length of referral time (i.e., time from arrest until MHC enrollment). One study found that Whites take significantly longer to process than non-Whites (Redlich, Liu, Steadman, Callahan, & Robbins, 2012) in MHCs. The most robust factor, however, to influence this length of time was pretrial detention status. Thus, because non-Whites are more often detained than Whites (Thomas, Benzeval, & Stansfeld, 2007), this above finding may reflect non-Whites’ pretrial detention status, which is associated with much quicker processing.
Once in the court, some MHC participants are successful and some are not. For example, Redlich, Hoover, Summers, and Steadman (2010) determined that whereas an average of 47% had graduated within a 2- to 4-year period, 30% of MHC clients had been terminated. In examining what predicts success/failure rates in MHCs, the impact of demographic factors, including race/ethnicity, has been mixed. On one hand, within MHC samples, race/ethnicity has not significantly affected receipt of sanctions while in the court (Callahan, Steadman, Tillman, & Vesselinov, 2013), court completion (Comartin, Kubiak, Ray, Tillander, & Hanna, 2015; Redlich et al., 2010), treatment utilization within the first 6 months of the court (Luskin, 2013), or rearrest rates while in the court or postcompletion (Herinckx, Swart, Ama, Dolezal, & King, 2005; Hiday & Ray, 2010; Hiday, Ray, & Wales, 2015).
However, on the other hand, there are some studies that found race/ethnicity to influence outcomes. Specifically, Dirks-Linhorst, Kondrat, Linhorst, and Morani (2013) found that minority status was associated with a 101% increased risk of termination from a municipal MHC, though receiving a new criminal charge while in the MHC associated with an 1198% increased risk of termination. And Ray and Dollar (2013) determined that whereas race of MHC clients was never mentioned in court compliance meetings (which they systematically observed) by team members, race, in combination with gender, was influential in MHC completion rates. Specifically, when examining the interaction between race and gender of MHC clients, minority men were more than five times more likely to be terminated than White women.
In sum, there is some evidence, albeit inconsistent, that racial minority/ethnicity status influences referral, acceptance, and success in MHCs. Less research has examined whether, once in MHCs, minorities are less likely than nonminorities to utilize and engage in community treatment. As mentioned above, there is a growing body of studies indicating that minorities are less likely to use behavioral health services compared with their White counterparts.
Race/Ethnicity and Treatment
Several studies have investigated relations between minority status and service utilization. For example, Ojeda and McGuire (2006) found that Hispanics and African Americans use outpatient behavioral health services significantly less often than Whites. Recently, one meta-analysis study of more than 50 studies concluded that there are racial disparities in health care utilization regardless of specific health problems (e.g., physical, mental, smoking, substance use; Williams & Mohammed, 2009). Beyond service utilization, racial/ethnic minorities have been found to have barriers for most treatment procedures, such as engagement, assessment, and communication (Atdjian & Vega, 2005). In addition, racial/ethnic minorities report that they have higher rates of mental health issues, as well as unmet treatment needs compared with Whites (Harris, Edlund, & Larson, 2005).
The literature has reported mixed outcomes for the role of the criminal history in behavioral health services use. On one side, it has been suggested that criminal history may serve as an obstacle in accessing behavioral health services (Weisman, Lamberti, & Price, 2004) and lead to less improvement in symptoms (McGuire & Rosenheck, 2004). In addition, Lee, Matejkowski, and Han (2017) found that African Americans and Hispanics with severe mental illness and criminal histories were less likely to receive mental health services compared with their counterparts. On the other side, engagement in the criminal justice system may enable offenders to receive more treatment compared with those not involved in the justice system. For example, to provide a systematic approach toward offenders with behavioral health problems, specialty courts were established to divert offenders into community treatment (Thompson, Osher, & Tomasini, 2008). Thus, participation in these programs aims to increase linkages to community behavioral health services (Luskin, 2013; Marinelli-Casey et al., 2008).
Indeed, one of the main criticisms of MHCs and other specialty programs is the potential for net-widening; that is, arresting people simply to allow them entry into the specialty program, and thus entry into treatment (Seltzer, 2005). Furthermore, participation in court diversion programs may hinge upon perceptions related to treatment amenability and motivation. For example, Luskin and Ray (2015) discussed how the Marion County, Indiana MHC team uses “motivation for treatment” as one of several factors considered when deciding whether to admit referred candidates for the court. The San Francisco MHC states in their policies and procedures manual that “participation . . . is voluntary and the defendant must be willing to participate in community treatment.” Relatedly, the voluntary nature of MHCs (see Redlich, 2005) may in a sense account for willingness to engage in mental health and/or substance use treatment. That is, to the extent that potential MHC clients are informed about the mandated treatment policies that typify MHCs, those choosing to enroll may be seen as more willing to enter treatment than those who choose not to enroll.
Overall, disparities in behavioral health service utilization among racial/ethnic minorities and people with criminal history are well-known. However, it is not yet clear whether this pattern of racial/ethnic disparities of behavioral service-use extends to MHC participants. Because service use is a key component of MHCs, both in their selection and outcome criteria, the questions addressed in the present study are important to answer.
The Present Study
To examine whether there are racial/ethnic disparities in MHC and traditional court community treatment utilization, data from the MacArthur MHC Project (see Steadman et al., 2011) were analyzed. The MacArthur MHC study was a comprehensive undertaking involving participants from four MHCs, comparison samples from each of the four sites, self-report interviews conducted at baseline entry into the MHC or the criminal justice system and again 6 months later. The main goal of the MacArthur MHC project was to determine if MHCs lead to improved criminal justice and behavioral health outcomes for offenders with mental illness as compared with traditional courts. To that end, participants from both court types were interviewed for more than 1 hr (at entry into the court/system and 6 months later) on a variety of topics, including housing and employment history, access to services, benefits, and so on. Variables used here are from the follow-up self-report interview, but also include the measures captured at entry to control for receipt of previous outpatient services.
The Andersen Behavioral Service Utilization Model (Aday & Andersen, 1998) has been used to examine individual factors associated with health services usage among diverse racial/ethnic groups in terms of predisposing characteristics (e.g., age, gender, marital status), need (e.g., mental illness), and enabling factors (e.g., insurance, employment; Babitsch, Gohl, & von Lengerke, 2012). Our research questions were as follows: Is there a difference by race/ethnicity in community treatment usage between traditional court participants and MHC participants? and What is the effect of MHC participation on service utilization within race/ethnicity? To answer these questions, the present study examines the extent to which racial/ethnic factors associate with the use of community behavioral health services, as well as with related service-use predictors in offenders with mental illness involved in MHCs or the traditional justice system.
Method
Participants
MacArthur MHC participants included in the present analyses are from San Francisco County, California (n = 132); Santa Clara County, California (n = 210); Hennepin County, Minnesota (n = 171); and Marion County, Indiana (n = 168). These MHCs accept offenders charged with misdemeanors or felonies, and persons with or without co-occurring substance use disorders. Demographic, including race/ethnicity, and diagnostic information was provided by the MHC or jail personnel. The traditional court sample (TAU; treatment-as-usual) were offenders in the same jurisdiction as the MHC, identified as having mental health problems by the county jail, but who were never referred into, nor rejected from, the MHC. For example, if a person had been rejected from the MHC, this person was ineligible to be in the TAU sample.
Descriptive statistics about participants can be found in Table 1. Among the 681 individuals in the present study, mean age was 37.11 years. The majority of the sample was male (59.0%); divorced, separated, widowed, or never married (70.6%); nonpublic benefit recipients (63.7%); unemployed (66.9%); have severe mental illnesses (86.7%); and use alcohol (81.7%) and drugs (52.1%). Approximately half of participants were White, both in the TAU and MHC (49.2% and 51.7%, respectively) samples, followed by African American (33.0% and 39.8%, respectively) and Latino/a (17.9% and 8.5%, respectively). MHC participants were likely to report using more mental health services, substance abuse services, public benefits, family contact, and days in the community compared with the TAU group. The TAU group was more likely to be married or have a significant other, to have a severe mental illness (defined as psychotic, bipolar, or major depressive disorder), use drugs, and have higher rates of symptoms compared with MHC participants.
Sample Characteristics (N = 681)
Note. TAU = treatment-as-usual; MHC = mental health court; CSI = Colorado Symptoms Index.
Measures
The main dependent variables were use of outpatient mental health and substance abuse services, as reported at the follow-up interview. Study participants were asked, “How many times did you receive [name of service] in the past 6 months (i.e., the time from entry into the MHC or justice system)?” In addition, participants reported the specific type of facility where they received the service. Respondents could answer as many times as they received the service in each facility. Outpatient facilities were defined as providing treatment without overnight availability; thus, hospitals, emergency rooms, residential facilities, and shelters were excluded.
Using the Andersen Health Service Utilization Model (Andersen, 1995), we investigated the association of predisposing, enabling, and need factors, with a particular focus on the impact of racial/ethnic background and behavioral service utilization. Racial/ethnic identity consisted of White, African American, and Latino (all which were coded to be mutually exclusive) but excluded the small samples of Asian (n = 12), American Indian (n = 20), Hawaiian (n = 5), and Others (n = 13; total = 6.75%). Although it would be meaningful to have included all the racial/ethnicities, we could not include them due to insufficient sample sizes.
Predisposing factors were age, gender, marital status, and education. Age was a continuous variable. We dichotomized marital status (a) as married or living with a significant other or sexual partner versus (b) not married, which included widowed, divorced, separated, or never married. For education, study participants reported the number of years of education completed, which was continuous in the multivariate models.
Enabling factors included self-reports of public benefits, employment, amount of family contact, previous mental health and substance use services, and days in the community. During the interview, study participants were asked whether they received public benefits including Medicaid, Social Security, or Social Security Disability Insurance. We dichotomized public benefits to indicate whether or not they received those benefits (yes = 1). Employment status was whether study participants worked either full-time or part-time during the past 6 months (employed, yes = 1).
Previous mental health and substance use services were measured at entry into the MHC or criminal justice system where treatment was received (e.g., emergency room, jail) and how many times. Days in the community were measured by asking for the type of residence and duration in the past 6 months. Any residence where outpatient services were not available were excluded, such as jails and prisons. All days in the community were summed and included in the model to account for the time that study participants were able to access community-based services. Another purpose to include the number of previous services and days in the community was to control for differences between the MHC and TAU groups. As diversion programs, MHCs aim to increase the likelihood of participants staying longer in the community, which in turn increases the possibility of receiving outpatient services.
Need factors included severe mental illness, alcohol use, drug use, and symptomatology. Psychiatric diagnoses were collected from the court and jails, and a binary variable of severe mental illness was created (bipolar disorder, depression, or schizophrenia = 1, all others = 0). Because of the strong correlation between treatment utilization and substance abuse (Hatzenbuehler, Keyes, Narrow, Grant, & Hasin, 2008; Masson, Sorensen, Phibbs, & Okin, 2004), having a history of alcohol and drug use was also included in the study models. Symptomatology was measured at entry into the court/system using the Colorado Symptoms Index (CSI; Conrad et al., 2001). The measure includes 15 statements concerning psychological or emotional difficulties (e.g., “In the past month, how often have you felt nervous, tense, worried, frustrated or afraid”, “In the past month, how often did you feel like hurting or killing yourself”). A 5-point Likert-type scale was used, with 0 (not at all) to 4 (at least every day). The CSI has been used with thousands of offenders with mental illness and in various settings, and has been found to be sensitive and internally consistent (Broner, Mayrl, & Landsberg, 2005; Draine, Blank, Kottsieper, & Solomon, 2005; Fischer, Shinn, Shrout, & Tsemberis, 2008). In this study, Cronbach’s alpha for the CSI was 0.85.
Data Analysis
Both dependent variables (i.e., community mental health and substance abuse treatment usage) showed overdispersion. For example, Kurtosis was 24.08 and 35.83 for mental health and substance abuse treatment, respectively, which indicates an abnormal distribution (West, Finch, & Curran, 1995). This is due to participants with no mental health services (53% for traditional court participant, 30% for MHC participants) and no substance abuse services (83% for traditional court participant and 72% for MHC participants). To handle overdispersion, it is usually recommended to categorize the variable and use negative binomial regression (Hilbe, 2011; Ver Hoef & Boveng, 2007). Thus, we first categorized into 10 groups (0 [0], 1-10 [1], 11-20 [2], 21-30 [3], 31-40 [4], 41-50 [5], 51-60 [6], 61-70 [7], 71-80 [8], 81-90 [9], 91-higher [10]). These transformed variables were used in the regression analyses. Then, we implemented negative binomial regression analysis to handle counter variables with many 0s (Allison, 2009). A log-likelihood test indicated that negative binomial regressions for the study models had better model fit than the Poisson regression (p < .001). In addition, some independent variables showed underdispersion (e.g., severe mental illness and alcohol usage). However, there are no assumptions related to the distribution of independent variables in any type of regression model (Weisberg, 2005). In addition, there are no influential outliers because they are dichotomous.
We conducted all statistical analyses using STATA version 13. To compare sample characteristics by racial/ethnic identity, we conducted either ANOVA or MANOVA tests depending on the nature of the study variables. To examine racial/ethnic variation in study groups, significant interaction terms between racial/ethnic identity and MHC/TAU found in the preliminary models were examined. The decision to retain interaction terms in the final models was based upon Wald chi-square statistics of two models (one with and one without interactions; Jaccard, 2001). Multicollinearity was examined using a conservative cutoff point of variance inflation factors (VIF; > 4.0; Fox, 1991).
Results
Table 1 includes descriptive statistics for participants by racial/ethnic identity and the results of the group comparison test. Study participants received approximately 11.7 and 6.5 times of mental health and substance abuse services, respectively. The results of chi-square or ANOVA tests indicate significant differences by racial/ethnic subgroups for three predisposing factors (age, gender, and education), three enabling factors (public benefit, employment status, and days in the community), one need factor (drug use), and data collection site. No other significant racial/ethnic disparities emerged (p > 0.05).
Table 2 presents correlations among the study variables. Family contact was positively associated with employment, days in the community, and being in the MHC group, and was negatively associated with severe mental illness and drug use. In addition, employment was negatively related to having a severe mental illness and drug use, but positively associated with days in the community. Interestingly, drug use was also negatively correlated with days in the community. Being in the MHC group was negatively associated with drug use and CSI scores.
Correlations Matrix of Observed Variables
Note. CSI = Colorado Symptoms Index.
p < .05. **p < .01. ***p < .001.
Table 3 shows the results of the negative binomial regression models. Negative binomial models were conducted separately for mental health and substance abuse services, and for each group. Within the TAU group, African Americans were less likely to receive mental health services compared with Whites. Study participants in Santa Clara and Hennepin Counties were less likely to receive mental health services compared with those living in San Francisco County. In addition, we found that people receiving public benefits and staying longer in the community were more likely to use mental health services in comparison with their counterparts. In addition, people using drugs were less likely to report receiving mental health services, whereas those with higher CSI scores were more likely to report using mental health services. In terms of substance abuse services, in the TAU group, a main effect of racial/ethnic identity was found, indicating that African Americans were less likely to use community substance abuse services compared with Whites. Similar to mental health services, people in Santa Clara and Hennepin Counties were less likely to receive substance abuse services than those living in San Francisco County. We also found significant effects of previous service use on substance abuse services, such that people with higher amounts of previous services were more likely to use substance abuse services 6 months later.
Predictors of Behavioral Health Service Use Among Different Racial/Ethnic Groups in TAU and MHC
Note. TAU = treatment-as-usual; MHC = mental health court; CSI: Colorado Symptoms Index; CI = confidence interval.
When examining within the MHC group, there was no significant effect of race/ethnicity on mental health or substance use outpatient services. Participants living in Santa Clara and Hennepin Counties were less likely to receive mental health services than those living in San Francisco County. However, MHC respondents in Marion County were more likely to use mental health services. In addition, for mental health services, MHC respondents with public benefits, more family contact, and more days in the community were more likely to use mental health services compared with their counterparts. Regarding substance abuse services, there were significant effects of previous services and days in the community, indicating that MHC respondents with more previous services and more days in the community were more likely to use the substance abuse services compared with their counterparts. MHC respondents living in Hennepin County were less likely to receive substance abuse services than those living in San Francisco County.
Table 4 shows the results of three different negative binomial models with the entire study sample. Model 1 included only racial/ethnic group, data collection site, and predisposing factors (i.e., age, gender, marital status, and education). In Model 2, enabling and need factors as well as study group (MHC or TAU) were included. In addition, two interaction terms (i.e., study group and racial/ethnic identity) were included in Model 3 to examine the combined effects of racial/ethnic identity and study group on outpatient behavioral health service utilization. In Model 1, there was no effect of racial/ethnic background on mental health and substance abuse utilization. However, there were significant effects of data collection site on mental health and substance abuse services. In addition, older people were more likely to receive mental health services compared with younger people.
Predictors of Behavioral Health Service Use Among Different Racial/Ethnic Groups (n = 681)
Note. CSI = Colorado Symptoms Index; MHC = mental health court; CI = confidence interval.
In Model 2, we found a significant effect of race/ethnicity, such that African Americans were less likely to receive mental health services compared with Whites. Similar to Model 1, there were effects of site on outpatient behavioral health service utilization (i.e., mental health and substance use service). In addition, there were significant main effects of public benefits, days in the community, drug use, and CSI; respondents on public benefits, with more days in the community, drug users, and higher CSI scores were more likely to use outpatient mental health services in comparison with their counterparts. Last, being in the MHC group increased the probability of using mental health services in comparison with being in the TAU group. In terms of substance abuse services, having more previous substance abuse services and days in the community increased the probability of later substance abuse service utilization.
In Model 3, receiving public benefits, more days in the community, increased CSI scores, and being in the MHC increased the probability of using mental health services. However, those using drugs were less likely to report using mental health services. For substance use treatment in the MHC group, there is significant effect of race/ethnicity such that African Americans were less likely to report using substance abuse services compared with Whites. However, African Americans in the MHC group were more likely to report receiving substance abuse services than African Americans who remained in the traditional justice system. In addition, having previous services and more days in the community increased the probability of receiving substance abuse services. Interestingly, respondents in Santa Clara and Hennepin Counties were less likely to report using both mental health and substance abuse services compared with those in San Francisco County, across the three models. 1
Discussion
Using data from the first multisite MHC study with a comparison sample, the present study examined racial/ethnic disparities in community behavioral health service utilization. Study findings suggested that there were significant racial/ethnic disparities in the utilization of both mental health and substance abuse services. More specifically, in the TAU group, we found that African Americans accessed both mental health and substance abuse services significantly less often than Whites. However, similar racial/ethnic disparities for community treatment were not found in the MHC group (see Table 3). In addition, racial/ethnic disparities remained in the interaction model (Model 3: Racial/ethnic × Study group), in that African Americans were less likely to use substance abuse services. However, African Americans in the MHC group were more likely to receive substance abuse services compared with African Americans in the traditional court system (Table 4). These findings suggest that MHCs not only increase service utilization like other studies have found (Boothroyd, Poythress, McGaha, & Petrila, 2003; Luskin, 2013; Olesen, Butterworth, Leach, Kelaher, & Pirkis, 2013), but may also significantly reduce racial/ethnic disparities in behavioral health service utilization among people mandated to these specialty courts.
Race/Ethnicity, Justice Involvement, and Treatment
It has been well established that racial/ethnic disparities exist in behavioral service utilization (Alegria, Vallas, & Pumariega, 2010; Cook, Barry, & Busch, 2013; Merikangas et al., 2011), which may be due to a lack of cultural competence or cultural perceptions of stigma related to mental health (Lee et al., 2017). Minority status is also one of the strongest predictors of dropout or absence in treatment (Fourth National Mental Health Plan Working Group, 2009; Mental Health Council of Australia, 2007; Twamley, Jeste, & Lehman, 2003). Such racial/ethnic disparities have also been found, and may even be exaggerated, for those involved in the criminal justice system. Specifically, studies show that having a criminal history can serve as a barrier to receiving community mental health services (Draine, Wolff, Jacoby, Hartwell, & Duclos, 2005). In addition, service providers often exclude offenders to avoid problems for the agency or because of a perceived inability to pay for services (Lamb, Weinberger, & Gross, 2001). At the same time, research has clearly demonstrated that offenders (the majority of whom are minorities; Alexander, 2012) have higher rates of mental health problems than those found in the community (Baksheev, Thomas, & Ogloff, 2010; Ogloff, Talevski, Lemphers, Wood, & Simmons, 2015). Thus, arguably it is minority offenders, as a group, who are in most need of treatment, but may be least likely to seek it out and/or receive it. Indeed, Paul and Moser (2009) found that African American and Latino/a youth involved in the justice system had significantly lower rates of mental health service utilization than White youth who were not involved in the justice system.
Mandated treatment programs, like Drug Courts and MHCs, may help to ameliorate racial/ethnic disparities in behavioral service use. For example, Jacobson, Robinson, and Bluthenthal (2007), and Johnson and Butrica (2012), in examining completion of outpatient treatment for people with addiction problems, found that African Americans were less likely to complete the treatment. However, even after controlling for race/ethnicity, patients referred by the court or criminal justice system were 1.98 times more likely to complete the outpatient treatment than self-referred participants. Similarly, we found in the current study that involvement in MHCs decreased racial/ethnic disparities in behavioral health service utilization, though only for African Americans.
The question then becomes why do MHCs and other forms of mandated treatment reduce racial/ethnic disparities in treatment utilization? Of course, one obvious answer is that once in the court, treatment is required, and thus the barriers that can discourage minorities (e.g., cultural stigma and cultural competencies) from accessing treatment may not have the same deterrence effect (though they still may be present). Moreover, although treatment is typically mandatory in these specialty courts, enrolling in the courts initially is not (Redlich, 2005). Thus, there may be qualitative differences in treatment motivations between those in and not in MHCs. In another study, however, which also used the MacArthur MHC data (i.e., Han & Redlich, 2015), baseline treatment motivation scores did not appreciably differ between the TAU (M = 23.87) and MHC (M = 24.80) samples (on a 70-point scale). It is also important to note that although there is some evidence that MHCs may have racial/ethnic differences in who is referred into MHCs, these same differences were not found among those accepted into the court (Steadman et al., 2005).
Another possible explanation for the racial/ethnic disparity found in the TAU sample, and not found in the MHC, could be due to the different approaches (e.g., personnel and system) toward offenders in traditional court versus court diversion programs. Unlike traditional courts, MHCs are based on the tenets of therapeutic jurisprudence (Winick, 2002; Winick & Wexler, 2003), which theorizes that the law and legal actors can be effective therapeutic change agents. MHCs have also been found to be higher in perceived procedural justice (Poythress, Petrila, McGaha, & Boothroyd, 2002) than traditional courts. MHC judges work toward ensuring the transparency of court decisions, increasing accountability for both participants and service providers, and emphasizing the dignity, respect, and voice of the participants (Wales, Hiday, & Ray, 2010). A recent study also found constructs of therapeutic jurisprudence (i.e., court knowledge, perceptions of procedural justice, and voluntariness) significantly increased rates of MHC compliance, which in turn, led to the successful completion of the court (Redlich & Han, 2014). Thus, to the degree that MHC compliance reflects treatment utilization (a main component of MHC participation), the therapeutic nature that often define these courts may play a role in reducing racial/ethnic disparities by providing appropriate services for all participants deemed fit for the court. Indeed, Ray and Dollar (2013) did not find that race on its own influenced MHC termination (in effect, the opposite of compliance) rates; 45% to 55% of minority men and women, and White men were terminated (see also Redlich et al., 2010), rates which stood in stark contrast to the 14% of White women who were terminated. Furthermore, whereas gender was discussed often in the MHC compliance meetings observed by Ray and Dollar (2013), race was never mentioned.
Other Factors Associated With Treatment Usage
Other than racial/ethnic disparities, there were five additional, significant predictors of behavioral health service utilization. First, one of the consistent findings was the variation between the sites, such that offenders living in San Francisco County received more behavioral health services (i.e., mental health and substance abuse) compared with those in Santa Clara and Hennepin Counties. Although speculative, these variations may stem from the sizes and urban/rural makeup of the counties; San Francisco is only 48 square miles whereas Santa Clara and Hennepin are 1,290 and 1,212 square miles, respectively. Previous studies have found that the distance to service provider is a significant factor related to the health service utilization (Gregory et al., 2000; Towne, Smith, & Ory, 2014). People living closer to the clinic may receive more services compared with those living farther away. Furthermore, urban areas typically provide more accessibility in terms of the transportation, health care systems, and so on. For example, Casey, Call, and Klingner (2001) found that urban residents were more likely to receive the health services compared with rural residents due to the delivery of health care services. Thus, people living in San Francisco, which is mainly urban, may receive more services because they have better accessibility to the behavioral health services in comparison with those living in the other counties included in the present study, which cover urban, suburban, and more rural areas.
Second, we also found that participants receiving public benefits in both traditional court and the MHC were more likely to receive mental health service than those who were not receiving them. This finding is consistent with other research which has found that having public benefits (Dooley, Fielding, & Levi, 1996; Dooley, Prause, & Ham-Rowbottom, 2000; Fryer, 1986) is associated with higher levels of behavioral health service utilization. Third, MHC participants with more family contact had higher levels of mental health service utilization, which also parallels previous findings (Sullivan & Von Wachter, 2009). In fact, support from significant others often encourages patients to continue their treatment (Hayes, Gray, & Edwards, 2008; Paul & Moser, 2009; Silver & Miller, 2003), and can significantly affect criminal behavior and mental health symptoms (Caldwell, 2010). Fourth, the longer study participants stayed in the community, the more likely they were to receive mental health and substance abuse services in both study groups, as expected. Still, MHC participants reported receiving more behavioral health services in the community than TAU participants, which supports the idea of the MHC as a diversion program, providing more opportunities for community-based treatment/services compared with the traditional court system. Fifth, previous services significantly predicted substance abuse service utilization for both traditional court and MHC participants. This finding may indicate that previous substance abuse services serve as a proxy indicator for having chronic substance abuse problems, and thereby having a higher need for those services. Similarly, we found that MHC participants with active drug use were more likely to utilize substance abuse treatment.
Limitations and Conclusion
To our knowledge, the present study was the first to examine possible racial/ethnic disparities in behavioral health service utilization in MHCs as compared with traditional courts. By testing the models developed, we were able to provide an as yet unrevealed role of MHC—that is, decreasing racial/ethnic disparities in mental health and substance abuse service utilization. However, as one empirical study, findings are preliminary and may not generalize to all MHCs. In addition, the study did not investigate the service utilization of mental health and substance abuse service use in the long-term (i.e., longer than 6 months) or after participation in the MHC ended. Thus, longitudinal analyses are needed to explore relations in more depth. Last, the data did not allow for inclusion of the characteristics of the community where study participants lived (e.g., resources for services) but are known to associate with individual behavioral service utilization. However, it is unclear whether community resources themselves would be expected to differ by MHC–TAU status. We also only included minorities who were African American and Latino/a. Future studies, to the extent possible, should oversample from other minority groups.
Despite these limitations, there are several implications of our results for MHCs and offenders with mental illness. First, as an alternative approach to offenders with severe mental illness, MHCs have been successful in reducing rearrest rates and increasing the accessibility of services (Burns, Hiday, & Ray, 2013; Luskin, 2013; Olesen et al., 2013). In the present study, MHCs were found to play a critical role in reducing racial/ethnic disparities for African Americans, which have been long-standing issues in the criminal justice and behavioral health systems (Prause & Dooley, 2001; Saunders, 2008). As such, study findings highlight perhaps unintended but essential benefits of MHC toward minority offenders with severe mental illness, a marginalized and vulnerable population in the criminal justice system. In addition, the present study identified critical factors associated with behavioral health service utilization. For example, receiving public benefits and days in the community significantly increased mental health service usage. Furthermore, geographic variation was also found to significantly associate with treatment usage, such that people living in more concentrated areas (San Francisco County) were more likely to receive both mental health and substance abuse services compared with those in more sprawling areas.
The present study also has implications for Andersen’s Behavioral Health Utilization model. A systematic review of studies using Andersen’s model revealed that none to date have attempted to compare traditional court and specialized court systems (Babitsch et al., 2012). In adding to the frequently used predisposing, enabling, and needs factors in Andersen’s model (e.g., ethnicity, income, insurance, health status; Babitsch et al., 2012), the present study found a significant impact of court system and geographic characteristics on behavioral health service utilization. Thus, future studies using Andersen’s framework may need to consider these factors when investigating the correlates of health care service. More importantly, the present study expands Andersen’s framework to apply to people involved in court diversion programs. To date, only a handful of studies using Andersen’s model have focused on people in the justice system. Although Andersen’s model was developed to examine health care utilization for vulnerable populations, people with criminal history have remained understudied. Thus, the present study has expanded the utility of the Andersen model, and preliminarily determined that MHCs can reduce racial/ethnic disparities known to plague both the criminal justice and behavioral health systems.
Footnotes
Acknowledgements
The authors wish to thank Dr. Henry J. Steadman and Policy Research Associates, as well as Professor John Monahan and the John D. and Catherine T. MacArthur Foundation Network on Community Mandated Treatment for generously supporting the original research.
The present research was partially supported by a 2014 NARSAD Independent Investigator Grant from the Brain & Behavior Research Foundation.