Abstract
Objectives. To examine the dual disproportionality that individuals with serious mental illness and people of color (PoC) occupy in the criminal–legal system.
Methods. This study follows a cohort of 623 individuals who screened positive for mental health issues at booking in 8 Midwestern jails in 2017. We followed individuals through the jails’ practices of jail-based mental health treatment, and we used Medicaid billing data to assess community-based behavioral health treatment engagement in the postyear period after jail release. The aim was to examine if an individual’s race/ethnicity was associated with their access to jail- and community-based mental health treatment.
Results. We did not find any racial disparities in jail-based treatment, although 3 community-based outcomes significantly differed. Compared with PoC, White people had 1.9 times greater odds of receiving community-based mental health and substance use treatment and 4.5 times greater odds of receiving co-occurring disorder treatment.
Conclusions. Barriers that individuals released from jail face adversely affect PoC, resulting in reduced access to treatment. Critical race theory can expose the assumptions and functions of systems of care and the possible reproduction of implicit bias in potential solutions.
By the end of 2016, approximately 6.6 million individuals were under community supervision or incarcerated in the United States; of this population, about 745 200 were incarcerated in jails.1,2 Jail racial compositions have changed over the last 10 years, with the percentage of (non-Hispanic) White people increasing and the percentage of (non-Hispanic) Black people decreasing.2 However, Black people are disproportionally overrepresented in jails, making up about one third (33.6%) of those incarcerated and yet composing around 13% of the general population.2,3 Those with serious mental illness (SMI) are also disproportionality represented in jails, with 1 in 4 likely to have an SMI (measured by a validated nonspecific psychological distress scale, the Kessler-6), which is 5 times greater than the rates for SMI (defined as serious functional impairment) among the adult general population.4,5 Individuals with a substance use disorder (SUD) are also overrepresented in jails, with two thirds (63%) of individuals incarcerated in jails (IIJ) having an SUD, compared with 5% of adults in the general population.6 Individuals with SMI or SUD, as well as people of color (PoC), occupy a space of dual disproportionality in the criminal–legal system.
Prior studies have sought to examine racialized differences in SMI prevalence and treatment engagement for IIJ while inside the carceral setting. A consistent finding in the literature is a higher prevalence of SMI using both objective screening tools (Kessler-6) and self-report measures (diagnoses) among White people incarcerated in jail (31% and 57%, respectively) compared with PoC incarcerated in jail (22% and 31%, respectively).4 Mental health (MH) treatment access while inside jail differed by race as well. Among IIJ with a prior MH history (i.e., self-reported mental or emotional condition or prior mental hospitalization), 44.7% of White people received some type of treatment compared with 34.2% to 40.6% of PoC.7 A more recent study, which operationalized SMI as a diagnosis of schizophrenia or bipolar disorder, found that compared with White Americans, African Americans were 5% less likely and Asian Americans were 10% less likely to receive jail-based treatment.8 In another study, among IIJ who were considered in need of treatment based on the Personality Assessment Inventory, no racialized differences were found in request or enrollment in MH treatment during incarceration.9
Racial disparities in community-based MH treatment during the preincarceration period are also present. Among IIJ who demonstrated MH treatment need based on the Personality Assessment Inventory, White people self-reported higher rates of community-based MH treatment prior to the current jail stay than PoC.9 Specifically, 13.6% of White people reported prior psychiatric hospitalizations, 21.3% prior outpatient treatment, 27.3% prior mood medications, and 15.2% use of current mood medications, compared with Black people, who had lower utilization on each category (7.4% prior hospitalizations, 7.5% prior outpatient treatment, 10.7% prior mood medications, and 4.0% current mood medication).9 These findings are consistent with a study of 18 421 IIJ across 10 metropolitan jail sites in which racial disparities existed among those who reported prior MH, SUD, and co-occurring disorder (COD) treatment engagement, with White people more likely than PoC to utilize treatment.10
To date, no study has assessed racial disparities in community-based behavioral health treatment of IIJ with SMI in the postincarceration period. Earlier studies reported community-based MH treatment prior to the target jail stay, and most have assessed these practices in 1 jail setting.8–10 Other studies assessed the role that SMI plays in recidivism but did not describe racial differences among these rates.11,12 This exploratory study adds to the existing body of knowledge by following individuals into the community after the target jail stay to uncover how race/ethnicity is associated with engagement in both jail- and community-based MH treatment 1 year after release from 8 Midwestern jail facilities. It aimed to examine whether an individual’s race/ethnicity is associated with their access to jail- and community-based MH treatment. On the basis of prior researchers’ work, and the contributions of critical race scholars, we hypothesized that racial disparities would exist for both jail- and community-based MH treatment engagement.7–10
METHODS
This analysis is part of a larger study that began in 2014 when the research team was hired to evaluate 8 Midwestern county jail diversion pilot programs funded by a unit of state government. At that time, individuals admitted to these diversion programs were tracked for treatment engagement and recidivism outcomes in the year following the receipt of jail diversion services. In 2017, these same counties expanded their services into other areas of the criminal–legal system, switching the focus from a program-level outcomes evaluation to a systems evaluation. Jail staff screened and followed individuals for 3 months inside the jail. State-wide Medicaid claims data tracked behavioral health treatment engagement in the 14 months after leaving jail. The primary focus of the systems evaluation was to assess each jail’s “process-as-usual” in the identification of SMI, and its referral to and engagement in jail-based MH treatment.
Study Population
We merged 3 data sources at the individual level: (1) an instrument administered to individuals at jail booking; (2) administrative data from each jail regarding MH processes, as well as legal history and recidivism; and (3) Medicaid encounter data from the state’s Department of Health and Human Services. The variables used from these sources are described in the next section; additional details on how the variables were categorized are reported elsewhere.13 MH identification varied across jails, with each having its own practice as usual. One jail used the Kessler-6 as its identification practice, whereas 6 jails used a combination of questions related to prior MH treatment.14 In 1 jail, the identification was asking if the individual was suicidal. In the overall sample (n = 2856), the jails identified 623 individuals (21.8%) as having an SMI by the jails’ process as usual. Individuals who were identified as having an SMI (n = 623) became the sample for the current study. Although a second SMI screening and identification did not occur at the time of jail release, the study sample had an average length of stay in jail of just over a month (35.93 days; Table 1). It is therefore reasonable to assume that individuals will likely need services upon community reentry, especially given the mental health impact of incarceration.
TABLE 1—
Bivariate Analysis of Factors Associated With Individuals With Serious Mental Illness, by Race: 8 US Midwestern Jails, 2017–2018
| Total, No. (%) or Mean ±SD | PoC, No. (%) or Mean ±SD | White, No. (%) or Mean ±SD | P | |
| Total sample | 623 (100) | 248 (39.8) | 375 (60.2) | |
| Demographics | ||||
| Male | 441 (70.8) | 207 (83.5) | 234 (62.4) | < .001 |
| Age, y | 34.08 ±11.631 | 33.40 ±11.722 | 34.36 ±11.492 | .31 |
| Housing insecurity | 317 (52.7) | 110 (46.6) | 207 (56.7) | .015 |
| Metropolitan county | 357 (57.3) | 198 (79.8) | 159 (42.4) | < .001 |
| Behavioral health history | ||||
| Prior MH treatment or current Rx | 351 (56.3) | 112 (45.2) | 239 (63.7) | < .001 |
| CMH client | 244 (52.5) | 87 (46.0) | 157 (56.9) | .021 |
| K6 score | 7.80 (6.621) | 6.22 (6.135) | 8.88 (6.767) | < .001 |
| Positive K6 score | 262 ±42.1 | 83 ±33.5 | 179 ±47.4 | < .001 |
| Substance misuse | 331 (53.9) | 133 (54.5) | 198 (53.5) | .81 |
| COD | 157 (25.2) | 50 (20.2) | 107 (28.5) | .018 |
| Legal history | ||||
| Past y jail | 307 (49.3) | 125 (50.4) | 182 (48.5) | .65 |
| Felony charge | 246 (39.7) | 87 (35.4) | 159 (42.6) | .07 |
| Length of stay, d | 35.93 ±66.472 | 40.30 ±70.208 | 31.44 ±60.918 | .11 |
| Jail-based treatment | ||||
| MH referral | 579 (92.9) | 222 (89.5) | 357 (95.2) | .007 |
| MH treatment | 399 (64.0) | 176 (71.0) | 223 (59.5) | .003 |
| Diversion services | 62 (10.0) | 16 (6.5) | 46 (12.3) | .018 |
| Community-based treatment | 434 (100) | 175 (40.3) | 259 (59.7) | |
| MH treatment engagement | 192 (44.2) | 58 (33.1) | 134 (51.7) | < .001 |
| SUD treatment engagement | 175 (40.3) | 53 (30.3) | 122 (47.1) | < .001 |
| COD treatment engagement | 73 (16.8) | 13 (7.4) | 60 (23.3) | < .001 |
Note. CMH = community mental health; COD = co-occurring disorder; K6 = Kessler-6; MH = mental health; PoC = people of color; Rx = medications; SUD = substance use disorder. Sample size was n = 623.
Independent Variables
A screening instrument administered by the jails to individuals during booking captured all demographic characteristics. The key variable of interest in this study is race/ethnicity. Respondents—or, in some cases within 1 jail, staff—completed the race/ethnicity variable on the screening instrument, which included the following possible responses: White, Black, Latino, Native American, or Other. These responses were collapsed into “White” and “PoC.” We wanted a more nuanced analysis of race/ethnicity, but small sample sizes of Latino, Native American, and Other races (4.9% total) did not allow for statistical analyses. Finally, although it was the priority of the research team to have individuals self-identify their race/ethnicity on the screening instrument, some jails’ booking practices were solely electronic. Officers entered information into the database while conducting the standard booking procedures. The remaining demographic variables included gender (male or female), age (calculated by date of birth and jail booking date), housing insecurity, and county size (coded based on US Department of Agriculture population sizes).
Behavioral health history included 4 variables: Kessler-6 (K6) score, prior MH treatment or medications, substance misuse, and whether the individual was a community MH (CMH) client prior to jail booking. The K6 is a validated self-report screening tool. A K6 score of 9 or higher is correlated with SMI in jail-based populations.15 The score for each individual was blind to jail staff, and thus the process-as-usual hinged only on the jails’ identification practice. We gathered prior MH treatment or medication, as well as 2 validated measures that screen for substance misuse in primary care settings, from the screening instrument.16 CMH client was provided in the Medicaid encounter data and dichotomously coded if the individual had received 1 or more MH services from a CMH provider in the year prior to their jail stay.
We included 3 variables in legal history: past year jail, target jail stay felony charge, and length of stay in jail. We gathered past year jail, dichotomously coded, from the screening instrument, whereas felony charge, operationalized as the most severe charge for the target jail stay, and length of stay, measured in days, were provided by each jail.
Dependent Variables
The key outcomes analyzed in this study include the MH treatment that individuals received during their target jail stay and treatment they received once they transitioned to the community.
Jail-based mental health treatment. The American Psychiatric Association’s 2016 guidelines for jail MH practices note that these institutions should have processes for identifying SMI, referral to MH treatment, and assessments and treatment received in jail.17 In addition to these guidelines, this study assessed who received diversion services that were offered during the program evaluation phase of the study.
Using the process discussed in the “Study Population” section, we collected from the jail staff at each institution data on referral to, and engagement in, MH treatment of the sample identified as having an SMI. Prior to any data collection, the research team established data extraction procedures for these variables based on each jail’s processes (through review of electronic medical records or a tracking spreadsheet). Once the screening instruments were collected, a list of identifiers were returned to each jail, and staff were asked to note if an individual received either referral to or treatment by MH clinicians for a 3-month period after taking the screening instrument. MH staff operating diversion services in the jail were also provided with the sample identifiers and asked if the individual participated in diversion services in the same period.
Community-based mental health treatment.
We used Medicaid encounter data to determine MH, SUD, and COD treatment of individuals 14 months after they left jail. Current Procedural Terminology codes and dates of treatment were provided for every treatment related to an MH or SUD diagnosis code. When an individual received the same type of treatment on the same date, by the same provider, we counted it as 1 COD treatment. All others were counted as MH or SUD.
Statistical Analyses
We used bivariate analyses (χ2 test of independence and independent samples t tests) to assess for differences between White people and PoC by demographic, behavioral health, and legal histories, as well as the jail- and community-based treatment outcomes. To assess the impact race had on jail- and community-based MH treatment, we used logistic regression models for 6 outcomes that differed by race at the bivariate level: within-the-jail (1) referral, (2) treatment, and (3) diversion, and within-the-community (4) MH, (5) SUD, and (6) COD treatment engagement. Although they are significant at the bivariate level, we did not perform logistic regression models on diversion, as these can be considered rare events.18 For analysis regarding community-based outcomes, we reduced the sample from 623 cases to 434 cases after removing individuals who went directly to prison from the target jail stay (n = 35) and those who were not found in the Medicaid encounter data (n = 154). These 189 individuals were not significantly different by race, gender, or age compared with the follow-up sample. All independent variables that significantly differed by race at the bivariate level were included as control variables in the logistic regression models.
RESULTS
There were total of 623 individuals in this study: 39.8% (n = 248) were PoC and 60.2% (n = 375) were White people. We found no racial differences when comparing the jail identification practices by race: PoC (21.8%, n = 248) were as likely as White people (21.9%, n = 375) to be identified as having an SMI (t(1, n = 623) = 0.000; P > .05). Bivariate analyses regarding outcome variables found that White people were more likely to have received a jail-based referral (95.2%, n = 357; χ2(1) = 7.347; P < .01) and diversion services (12.3%, n = 46; χ2(1) = 5.633; P < .05) compared with PoC (89.5%, n = 222; 6.5%, n = 16, respectively). PoC were more likely to have received jail-based MH treatment (71.0%, n = 176; χ2(1) = 8.575; P < .01) than were White people (59.5%, n = 223). For community-based behavioral health treatment engagement, White people engaged in MH treatment (51.7%, n = 134; χ2(1) = 14.638; P < .001), SUD treatment (47.1%, n = 122; χ2(1) = 12.276; P < .001), and COD treatment (23.3%, n = 60; χ2(3) = 18.487; P < .001) at greater proportions than PoC (33.1%, n = 58; 30.3%, n = 53; 7.4%, n = 13, respectively; Table 1).
We performed 5 logistic regression models to assess the correlates of MH outcomes, with race being the variable of concern. After we controlled for significant factors that differed by race at the bivariate level, race was not significantly associated with the 2 jail-based models (Table 2). Race was significantly associated with community-based MH treatment engagement (χ2(1) = 5.505; P < .05). The model was able to successfully predict 76.3% of cases and explain 43.4% of the model variance. White people had 1.9 times greater odds of engaging in community-based MH treatment (adjusted odds ratio [AOR] = 1.937; P < .05; 95% confidence interval [CI] = 1.111, 3.376) compared with PoC (Table 3). Race was also significantly associated with SUD treatment engagement (χ2(1) = 5.240; P < .05), successfully predicting 74.6% of cases and explaining 37.0% of the model variance. White people had 1.9 times greater odds of engaging in SUD treatment (AOR = 1.865; P < .05; 95% CI = 1.090, 3.190) compared with PoC (Table 3). Lastly, race was also significantly associated with COD treatment engagement (χ2(1) = 17.152; P < .001), successfully predicting 83.7% of cases; the variance explained was 29.9%. White people had 4.5 times greater odds of receiving community-based COD treatment (AOR = 4.472; P < .001; 95% CI = 2.082, 9.605) compared with PoC (Table 3).
TABLE 2—
Logistic Regression Models of Predictors of Jail-Based Mental Health Treatment Engagement: 8 US Midwestern Jails, 2017–2018
| AOR (95% CI) | |
| Model 1: MH referrala | |
| Race/ethnicity: White | 0.77 (0.38, 1.57) |
| Male | 0.96 (0.44, 2.11) |
| Housing insecurity | 0.62 (0.32, 1.20) |
| Nonmetropolitan county | 4.10 (1.57, 10.68) |
| Prior MH treatment or current Rx | 0.71 (0.36, 1.43) |
| Positive K6 score | 0.79 (0.39, 1.58) |
| Substance misuse | 0.94 (0.47, 1.85) |
| Past y jail | 2.03 (1.02, 4.02) |
| Length of stay | 1.00 (0.99, 1.00) |
| Model 2: MH treatmentb | |
| Race/ethnicity: White | 0.72 (0.46, 1.14) |
| Male | 0.91 (0.59, 1.42) |
| Housing insecurity | 0.68 (0.45, 1.01) |
| Metropolitan county | 8.06 (5.12, 12.67) |
| Prior MH treatment or current Rx | 1.32 (0.86, 2.02) |
| Positive K6 score | 1.20 (0.80, 1.81) |
| Substance misuse | 0.72 (0.48, 1.07) |
| Past y jail | 1.78 (1.19, 2.66) |
| Length of stay | 1.01 (1.01, 1.02) |
Note. AOR = adjusted odds ratio; CI = confidence interval; K6 = Kessler-6; MH = mental health; Rx = medications. Sample size was n = 590.
χ2(9, n = 590) = 26.711, P = .002; Nagelkerke R2 = 11.2%, predicted cases = 93.1%.
χ2(9, n = 590) = 170.135, P < .001; Nagelkerke R2 = 34.3%, predicted cases = 77.1%.
TABLE 3—
Logistic Regression Models of Predictors of Community-Based Behavioral Health Treatment Engagement: 8 US Midwestern Jails, 2017–2018
| AOR (95% CI) | |
| Model 3: MH treatment engagementa | |
| Race/ethnicity: White | 1.94 (1.11, 3.38) |
| Male | 1.23 (0.72, 2.10) |
| Housing insecurity | 1.56 (0.95, 2.55) |
| Metropolitan county | 0.99 (0.57, 1.71) |
| Prior MH treatment or current Rx | 3.33 (1.95, 5.66) |
| CMH client | 8.33 (4.97, 13.99) |
| Positive K6 score | 1.33 (0.80, 2.23) |
| Substance misuse | 1.02 (0.61, 1.71) |
| Past y jail | 0.85 (0.52, 1.40) |
| Length of stay | 0.99 (0.99, 0.998) |
| Model 4: SUD treatment engagementb | |
| Race/ethnicity: White | 1.87 (1.09, 3.19) |
| Male | 1.62 (0.96, 2.72) |
| Housing insecurity | 1.47 (0.91, 2.38) |
| Metropolitan county | 0.67 (0.39, 1.16) |
| Prior MH treatment or current Rx | 1.14 (0.67, 1.94) |
| CMH client | 9.33 (5.53, 15.74) |
| Positive K6 score | 1.46 (0.89, 2.40) |
| Substance misuse | 1.04 (0.63, 1.69) |
| Past y jail | 0.85 (0.52, 1.38) |
| Length of stay | 0.99 (0.99, 0.998) |
| Model 5: COD treatment engagementc | |
| Race/ethnicity: White | 4.47 (2.082, 9.61) |
| Male | 1.17 (0.638, 2.13) |
| Housing insecurity | 1.66 (0.93, 2.97) |
| Metropolitan county | 2.06 (1.09, 3.89) |
| Prior MH treatment or current Rx | 2.25 (1.12, 4.54) |
| CMH client | 6.95 (3.22, 15.00) |
| COD | 0.70 (0.37, 1.31) |
| Past y jail | 0.96 (0.53, 1.73) |
| Length of stay | 0.99 (0.98, 0.998) |
Note. AOR = adjusted odds ratio; CI = confidence interval; CMH = community mental health; COD = co-occurring disorder; Rx = medications; SUD = substance use disorder. Sample size was n = 418 for COD and n = 413 for MH and SUD.
χ2(10, n = 413) = 161.564, P < .001; Nagelkerke R2 = 43.4%, predicted cases = 76.3%.
χ2(10, n = 413) = 132.509, P < .001; Nagelkerke R2 = 37.0%, predicted cases = 74.6%.
χ2(9, n = 418) = 82.463, P < .001; Nagelkerke R2 = 29.9%, predicted cases = 83.7%.
DISCUSSION
This exploratory, multisite study’s analysis of racial disparities in jail- and community-based MH treatment engagement found no differences in racial disparities in jail-based MH treatment, after controlling for other key factors. This finding is similar to the findings of some prior research and contrary to those of others.7–9 Racial disparities in community-based treatment engagement indicated that White people had a greater chance of engaging in treatment of MH, SUD, and COD compared with PoC upon release from jail.
Although race/ethnicity and behavioral health do not predict involvement in the criminal–legal system outright, institutional violence—state-sanctioned inequalities that cause (inter)personal violence—greatly affects PoC and individuals with SMI.19,20 Literature that discusses jail- and community-based treatment engagement for IIJ commonly separates barriers to treatment along 2 lines: (1) the individual’s intrapersonal attitudes (such as internalized stigma) toward treatment, as influenced and reaffirmed by dominant discourse; and (2) structural barriers to treatment (such as cost and availability), as constructed by inequalities resulting from systematic oppression.9 It is worth considering that the lack of racial disparities found in jails may be the unintended consequences of PoC being overdiagnosed, which may contribute to the individual’s attitudes toward treatment.21 However, among studies in which racial disparities in SMI prevalence and jail-based treatment were present, it is plausible that the inverse is true, the bias here being an underdiagnosis among PoC through the racial biases of MH screens.22
Regarding the individual’s intrapersonal attitudes, trust in institutions was found to be a barrier for individuals with COD (and SUD). It is possible that IIJ are coerced into MH treatment inside of jails because of the power differential between those incarcerated and those incarcerating. Upon release, an individual may disengage from treatment through distrust of CMH.23 Perceiving attrition between jail- and community-based treatment as “self-determination” obscures the impact of our racist reality, one in which PoC may not feel safe engaging in services. The perception of “choice” is further complicated, as not all those affected by incarceration rank health as a top priority, with housing, employment assistance, education, and assistance in getting benefits outranking physical and behavioral health.24 Furthermore, time—a social determinant of health—is racialized, with PoC being disproportionately harmed because they have lost time through incarceration and thus have less time to secure these community resources.25 This may create difficultly for providers if individuals who need treatment are less likely to solicit or engage these types of supports. Trauma may also create barriers to behavioral health treatment. Traumatic experiences are particularly high for IIJ.26 Compared with IIJ without SMI, IIJ with SMI are more likely to be unhoused in the year before arrest, have higher rates of physical and sexual abuse, and have familial histories of SUD or intergenerational incarceration, experiences that can traumatize individuals and communities.7,27
Limitations
Although the findings of this multisite study contribute to the literature on racial differences in community-based behavioral health treatment, limitations exist. First, there were some inconsistencies in collection of the race variable: it was usually the incarcerated individual who reported race/ethnicity, but at 1 jail, staff reported it. In addition, there are more categories within race/ethnicity than were offered to the IIJ or jail staff. Increased specificity on race/ethnicity within jails and carceral settings is necessary for future studies.28 Second, MH identification practices varied across the 8 jails, and concern exists regarding their accuracy in assessing SMI. There may be cases in which IIJ were misidentified as having a MH need, whereas others may have been missed who needed services. Future studies should include jails with reliable identification practices.
Third, the nature of administrative data does not allow for researcher input into data points for collection, and thus limits analyses. Fourth, the jail-based MH services collection is limited by the 3-month data collection allowed in the jails. Individuals who stayed in jail longer than 3 months (12.2%, n = 76) may have received such services after the study period. Fifth, we assessed our community-based behavioral health treatment engagement using Medicaid encounter data. Although a substantial proportion of the sample was found (n = 434, 72.6%), individuals covered by private health insurance and Veterans Affairs were missed (n = 154, 27.4%). Sixth, the operationalization of COD treatment engagement was highly conservative; therefore, it is possible that COD treatment engagement is higher in reality than was calculated for use in this study.
Seventh, the best approach to analyzing the nested nature of this data is multilevel modeling; however, the current number of jails did not provide enough level-2 power for this type of analysis. Eighth, although our regression models had high Nagelkerke R2 proportions (29.9% to 43.4%), other factors could contribute to the receipt of these treatments. For example, behavioral health treatment engagement has been associated with specific substances, as individuals who use more severe substances are more likely to receive treatment.10 Probation conditions may also influence who is receiving treatment. Discriminatory practices such as classism, ableism, and mentalism–sanism may also contribute to the unexplained variance. Finally, CMH agencies face multiple challenges in providing treatment, such as long wait lists, limited or restricted funding, and local transportation issues.9 Future work should consider the impact of such factors on treatment engagement after jail release.
Public Health Implications
Successfully addressing structural barriers to treatment of individuals with SMI affected by the criminal–legal system is often described as engineering “easy access” to community-based MH treatment and other necessary resources such as seeking and maintaining affordable housing.29 However, others take a more radical approach, calling for a redesign of the entire US health care system.30 A suggestion of the latter entails sophisticated case management programs and systems–organizations partnerships between corrections and local CMHs.30 Other suggestions for addressing the overrepresentation of individuals with SMI in the criminal–legal system include providing trauma-specific interventions, providing integrated COD treatment, connecting individuals to supported employment and housing providers, and, as appropriate, utilizing evidence-based practices.12,31 Furthermore, since the rapid connection to treatment after jail is critically important because of risks of suicide and overdose, the timing of such potential solutions must be considered in redesigning health care service delivery in the United States.32
Regardless of the specific barrier to behavioral health treatment, this study’s findings and the presence of such barriers are indicative of the legacy of mentalism, racism, and discrimination against those who have been incarcerated. Racial disparities are not surprising when operating from a perspective informed by critical race theory, which can assist in illuminating how current systems reproduce discourse informed by the “aftermath of slavery, labor exploitation, and racial discrimination.”33 Critical race theory articulates several principles that form its foundation:
Racism is ordinary (meaning that it is pervasive, ever-present, and continuously shaping the world).
White people often have little self-interest in dismantling racist structures because racism, in some ways, benefits them—termed the “interest–convergence hypothesis” or material determinism.
Race is a social—not biological—construct.
US dominant discourse has narrativized non-White groups differently over time while steadfastly remaining anti-Black—termed differential racialization.
The unique voices of PoC are important and should be centered.34
By applying a critical race theory lens to CMH and criminal–legal policies and practices, administrators and staff are better equipped to discern how efforts to dismantle structural racism may be stalled by implicit bias and are entangled in other forms of violence, such as classism and sexism. Efforts should include authentic leadership and engagement from PoC and culturally responsive mental health interventions, which can be up to 4 times more effective than nonculturally responsive engagement strategies and interventions.35 A measured approach to racism’s pervasiveness must become the operating framework for systems reform and intersystem coordination so that increasing MH engagement for PoC is but 1 viable contribution in realizing our shared commitment for racial justice.
ACKNOWLEDGMENTS
We thank the Michigan Department of Health and Human Services and the Governor’s Mental Health Diversion Council for funding this work.
We thank Brad Ray, PhD, for his feedback in getting this article ready for submission, and also the reviewers and editors for their helpful suggestions for revisions to the article.
CONFLICTS OF INTEREST
The authors declare no conflicts of interest.
HUMAN PARTICIPANT PROTECTION
The Wayne State University institutional review board reviewed both the program and systems evaluations and found the study to be nonhuman participation research because of its evaluative nature.
Footnotes
See also Canada, p. 178.
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