Literature Reivew

https://doi.org/10.1007/s12103-021-09629-6

Assessing the Effect of Mental Health Courts on Adult and Juvenile Recidivism: A Meta‑Analysis

Bryanna Fox1 · Lauren N. Miley1 · Kelly E. Kortright1 · Rachelle J. Wetsman1

Received: 26 December 2020 / Accepted: 25 May 2021

© Southern Criminal Justice Association 2021

Abstract Mental health courts (MHCs) are increasingly used across the United States as a means of reducing contact with the criminal justice system for individuals experi- encing serious mental health conditions. MHCs rely on diversion from incarcera- tion to rehabilitation, services, and treatment to reduce recidivism and other nega- tive outcomes among individuals with mental health disorders. While MHCs are a potential evidence-based remedy for the intensifying mental health and criminal justice crises in America, there is limited research indicating the overall effects these courts have on recidivism, and whether the effects vary across different sub-groups or research design and analytic features. Therefore, we present a meta-analysis of 38 effect sizes collected from 30 evaluations conducted from 1997 through 2020 on the impact of mental health courts on recidivism for adults and juveniles with mental health issues in the United States. Weighted meta-analytic results indicate that MHC participation corresponds to a 74% decrease in recidivism (OR = 0.26). Notably, the strength of MHC effects are similar for adult and juvenile participants, and stable across varied follow-up periods, study design features, and when prior criminal his- tory, gender and race/ethnicity are controlled for in the analyses. Implications for the criminal justice system are also discussed.

Keywords Mental health courts · Corrections · Courts · Diversion · Recidivism · Meta-analysis

Recent events, such as the tragic death of George Floyd in Minneapolis and the civil unrest it spurred, have led to a renewed focus on criminal justice reform across America. While reform efforts are vital to improving the justice system and out- comes for people the system is meant to engage and protect, there is a concurrent need to understand the efficacy of programs and policies proposed to address the foremost issues in the criminal justice system today.

* Bryanna Fox [email protected]

1 University of South Florida, Tampa, FL, USA

/ Published online: 14 July 2021

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To that end, a principal concern relates to the overrepresentation and adverse treatment of individuals with mental health issues who encounter the police, courts, and corrections systems. Specifically, while eight million Americans experience severe mental illness, these individuals are involved in one out of every ten calls for police service, they are one in every five people in U.S. jails and prisons, and are the victims of one in every four fatal encounters with police each year (Fuller et al., 2015). In fact, the risk of being killed by police is 16 times greater for those with mental illness than those without mental health issues (Fuller et al., 2015), and all but six states are housing more people with serious mental illness in correctional facilities than in state psychiatric hospitals (Torrey et al., 2014).

Consequently, efforts to address the disparate involvement in the justice system and improve outcomes for people with mental illness are a primary objective of recent calls for reform. Specifically, one major reform effort involves the increased use of mental health courts (MHCs), which are problem-solving courts designed to divert individuals with mental health issues away from incarceration, and towards rehabilitation and individualized treatment to address underlying mental health needs, and reduce current and future contact with the criminal justice system.

While MHCs are a promising potential remedy to the ongoing mental health and criminal justice crises in America, they are also costly, time consuming, and lim- ited research has examined their cumulative efficacy for reducing recidivism, and in particular, whether the effects generalize across different sub-groups and evaluative research designs. Therefore, the aim of this study is to examine the effectiveness of MHCs on future recidivism using a meta-analysis of all peer-reviewed empirical research from 1997 to 2020. Notably, this is the first meta-analysis to include evalu- ations of both adult and juvenile MHCs on recidivism, and examine the moderating effects of numerous methodological design and study sample features, in order to more accurately estimate the generality of MHC efficacy on future offending.

Mental Health Courts: Intervention vs. Incarceration

The psychiatric hospital ‘deinstitutionalization’ in the United States since the 1960s has ultimately led to a substantial increase in the proportion of individuals with mental health issues being detained in correctional facilities, with over half of those in U.S. jails formally diagnosed with or showing symptoms of a mental illness in the past year (James & Glaze, 2006; Trestman et al., 2007). While MHCs were not specifically designed as a response to deinstitutionalization, these types of mental health diversion programs were created as a response to the overwhelming number of people with mental illness engaged in the criminal justice system.

Mental health diversion programs take two forms: those which occur prior to booking, and those that take place afterwards. Pre-booking diversion programs are often referred to as co-response models or crisis interventions, and involve mental health providers responding to police calls (instead of or alongside police) and facil- itating access to mental health treatment and services for individuals in need, rather than making an arrest and/or booking the person in jail (Dewa et al., 2018). After an arrest and booking occurs, diversion options are restricted to the court system.

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MHCs represent one form of problem-solving courts, supervised by a sitting judge within a specialized docket, where qualifying individuals are diverted to commu- nity-based mental health treatment as an alternative to incarceration (Boothroyd et al., 2003; Moore & Hiday, 2006). The main goal of MHCs is to reduce recidi- vism by addressing underlying mental health concerns that are typically not met in jails and prisons (Baillargeon et al., 2009). Individuals who participate in the MHC diversion program receive constant support and connection to services designed to treat, rather than exacerbate or obfuscate, their mental health issues (Moore & Hiday, 2006; Redlich et al., 2010).

While MHCs are among the newer criminal justice reforms, with the first imple- mented in Broward County, Florida in 1997, MHCs are now the second most com- mon post-booking diversion program (after drug courts) in the United States (Strong et al., 2016). In fact, according to the U.S. Department of Health and Human Ser- vices, Substance Abuse and Mental Health Services Administration (SAMHSA), there are currently 477 MHCs in operation around the nation, with 421 serving adults and 56 serving juveniles with mental health disorders who become engaged with the criminal justice system (Treatment Court Locators, 2020). MHCs are only available to eligible individuals after an offense, arrest, and booking has taken place, although the hope is that by engaging in treatment to address the underlying men- tal illness, this intervention is able to better reduce the risk of recidivism, lessen the strain on the criminal justice system, and ultimately serve as a more effective option than adjudication in traditional courts and sentence to incarceration (Goodale et al., 2013; Miller & Perelman, 2009). While initial evidence suggests that MHC participants exhibited significantly lower recidivism rates and a longer time until re-arrest compared to those who go through the traditional court and corrections system (Moore & Hiday, 2006), the invariance in findings across participants, study designs, and follow-up period remains unclear (Honegger, 2015). However, in order to fully embrace MHCs as an effective evidence-based solution to one of the major issues facing the criminal justice system to date, a rigorous and comprehensive anal- ysis of the generality of MHC efficacy is necessary.

Mental Health Courts: Efficacy and Invariance in Effects

While MHCs have been in existence for just over 20 years, given the rapid prolifera- tion of these diversion courts across the nation, and intense interest in their effects by academics, practitioners, and policymakers, several studies evaluating their effi- cacy have already been undertaken. Notably, two initial meta-analyses synthesizing the results of these evaluative studies have been conducted (Lowder et  al., 2018; Sarteschi et al., 2011), providing a solid foundation for future meta-analytic work to build upon. While these meta-analyses indicate that MHCs have a small to moder- ate effect on recidivism in the intended direction, both examined only adult MHCs, and therefore no estimate of the mean effects of youth MHCs on recidivism has been evaluated. Furthermore, only a small sample of studies (e.g., n = 18, Sarteschi et al., 2011; n = 17, Lowder et al., 2018) were included in these early meta-analytic assess- ments, limiting generalizability of these findings. Finally, a very limited assessment

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of the potential treatment heterogeneity among MHC participants and evaluative methodological design features has taken place, leaving much unknown regarding whom, under what conditions, and in which study designs the observed effects can reliably be expected to occur. Nevertheless, the extant meta-analyses provide prom- ising results for MHCs that warrant further examination of these issues.

More specifically, in the first meta-analysis on the efficacy of adult MHCs on recidivism, Sarteschi and colleagues (Sarteschi et al., 2011) examined 18 MHC eval- uations conducted through July 2009, and estimated a cumulative mean effect size of Hedges’ g = -0.54. This suggests that overall, MHCs correspond to a moder- ate and statistically significant reduction in recidivism for the adults who received this program, compared to adjudication in the traditional court system. Examina- tion of methodological moderators indicated that “higher quality” studies produced a smaller mean effect (Hedges’ g = -0.52) than “lower quality” studies (Hedges’ g = -0.56). Consequently, Sarteschi and colleagues note that the limited sample size and the lower methodological quality of included studies could potentially lead to upward bias in the observed effect size results.

A follow-up analysis by Lowder et al. (2018) built on the work of Sarteschi et al. (2011) by extending the inclusionary period of research on adult MHCs through December 2015, and by conducting a broader array of methodological moderation analyses. This meta-analysis ultimately included 17 studies comprised of 19 unique effect sizes on the impact of adult MHCs on recidivism measured in four ways: arrest, charge, conviction, and jailing (Lowder et al., 2018). However, results of this study represent a notable departure from prior findings, as the authors found a much smaller cumulative mean effect1 of adult MHCs on recidivism (d = -0.20). Moreover, the moderation analyses revealed that MHCs had a larger effect on the risk of future charges (d = -0.36) and jailing (d = -0.36) versus arrest (d = -0.10) or conviction (d = -0.11). Length of follow-up period produced no variation in effects (d = -0.19 for one year and over one year follow-up periods), and “low quality” studies pro- duced much larger effect size estimates (d = -0.35) than “high quality” evaluations (d = -0.13). As before, Lowder and colleagues note that the limited availability of MHC evaluations reduced confidence in the reliability of the findings, and that more robust moderation analyses (e.g., by MHC participant features, study-level modera- tors such as methodological controls, etc.) should be conducted to better assess the generality of MHC effects on recidivism across a variety of contexts, study designs, and samples.

1 Hedges’ g is roughly equivalent to Cohen’s d, as both effect sizes represent the difference in means (M) on an outcome for the treatment and control conditions, divided by the standard deviation (SD). How- ever, as d= M1−M2

SD whereas g = M1−M2

SDpooled , a slight upward bias in d may occur when sample sizes are small

(i.e. n < 20) and greater variation in SD’s across samples may exist. Therefore, Hedges’ g utilizes a pooled SD weighted by sample size, and therefore produces a slightly more precise estimate of effects for small samples than Cohen’s d (Hedges, 1981).

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Current Study

Results of prior meta-analytic research on MHCs suggest that these diversion court programs may lead to small to moderate reduction in recidivism among adult partic- ipants. Given the prevalence of MHCs across the nation, and comparatively limited knowledge on the overall effects of MHCs on re-offending and potential variance in effects for youth versus adults and/or more sophisticated methodological designs, further meta-analytic research is necessary to systematically evaluate MHC effects on recidivism and potential variation across key sub-groups and evaluative study features. Therefore, the purpose of this study is to identify and analyze all of the cur- rently available research on the effect of adult and juvenile MHC diversion programs on future recidivism, and assess the generality of these effects using data from a larger and more recent sample of MHC evaluations. Thus, two primary research questions inform this analysis. First, what is the overall weighted effect of MHC par- ticipation on re-offending? Second, how does the effect of MHCs on recidivism vary by participant features, evaluative study design, and analytic model specifications? We detail the process and methods associated with our meta-analysis in the sections below.

Data and Methods

This study aims to build on prior meta-analytic research on MHCs by 1) expand- ing the search period to increase and update  the sample, 2)  including both adult and youth MHC evaluations in the analysis, and 3)  conducting more comprehen- sive moderator analyses to assess the reliability of findings across participant fea- tures, study design, and methodological rigor. To do this, we first aim to identify all relevant peer-reviewed evaluations of the effects of MHCs on recidivism pub- lished between January 1, 19972 and December 1, 2020 using a comprehensive search of major electronic databases and the reference sections of all identified articles. Specifically, searches of databases including the Cochrane library, JSTOR, Google Scholar, Criminal Justice Abstracts, Social Science Abstracts, Psychologi- cal Abstracts, and Social Work Abstracts were conducted using the keyword terms mental health courts and recidivism, and all related synonyms and variants such as mental health diversion, mental health problem-solving courts, mental health diver- sion program, post-booking diversion, mental illness, offending, crime, delinquency, violence, and evaluation in order to identify all suitable scientific publications to be included in the analysis.

2 We used 1997 as a starting point of our search given that this is the year that the first MHC was imple- mented.

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Inclusion Criteria

Next, a series of inclusion criteria were selected and applied to the studies identified in the initial searches. Specifically, potentially eligible studies were maintained for inclusion in the meta-analysis if they met the following three inclusion criteria:

1. The study must be a quantitative evaluation of the effect of an adult or juvenile mental health court diversion program3 on subsequent recidivism.

2. The study must contain an effect size (e.g., correlation, odds ratio, d) or raw data (e.g. treatment and control means and standard deviations) to calculate an effect size for this relationship between MHC participation and future recidivism.

3. The study must be an original evaluative assessment published between 1997 and 2020 in a peer-reviewed outlet such as academic journals, books, and book chapters.4

In total, 30 published peer-reviewed evaluations, which contained 385 unique effect sizes on the relationship between MHC program participation and future recidivism met each of the inclusion criteria for use in this meta-analysis. These arti- cles are denoted using an asterisk in the References section. A PRISMA diagram illustrating the identification, screening, eligibility, and inclusion stages is provided in Fig. 1.

Measures and Moderators

In meta-analyses, standardized effect sizes and associated estimates of variance are collected from all included studies and utilized to estimate the overall mean effect size in a weighted cumulative analysis (Borenstein et al., 2009; Lipsey & Wilson, 2001). As noted above, at least one effect size estimating the relationship between MHC participation and future recidivism was collected or calculated for each article in the sample. While the reported effect sizes included r (bivariate correlation), B (regression coefficient), Cohen’s d and Hedges’ g (standardized mean effects) and odds ratios, each of these were converted and standardized to odds ratios, given the dichotomous nature of the outcome measure, to increase comparability (see formulas in Borenstein et al., 2009; Lipsey & Wilson, 2001). When an estimate of

5 There number of effect sizes exceeds the total studies in the sample as certain studies contained multi- ple results, as they analyzed effects from multiple MHCs in the same publication.

3 This is operationalized as any specialized court-based diversion program for individuals with mental health conditions where support, services, and/or treatment are provided in lieu of incarceration and tra- ditional court adjudication. 4 In line with related meta-analytic research, we opted to include only peer-reviewed studies to increase the validity of the results, as unpublished or non-peer-reviewed studies may not reach the accepted bar of quality required for publication in the field, due to a lack of evaluation through the peer-review pro- cess. Moreover, selection bias is introduced when unpublished literature is included in the sample as it is impossible to ensure that all unpublished works are identified for inclusion.

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variance was not made available, estimated standard errors (SEs) were calculated6 so all effect sizes could be included in the meta-analysis.

Additionally, a host of items reflecting the MHC sample composition, study design, and methodological considerations for each of the 38 effect sizes in this analysis was coded in order to conduct comprehensive moderation analyses and test the generality of treatment effects across various contexts. Each of these items are described below and presented in Table 1.

Year of publication refers to the year in which the article each effect size cor- responds to was published. The years were separated into four categories for ana- lytical purposes: 2000–2004 (n = 2), 2005–2009 (n = 7), 2010–2014 (n = 15), and 2015–2019 (n = 17). It should be noted that since the previous  MHC meta-analy- sis spans only through 2015, as many as 17 new effect sizes (i.e. 45% of the total

Studies identified through database

searching

Studies screened and met basic inclusion

criteria

Studies excluded for not meeting basic

criteria

Studies met full inclusion criteria

(n= 30)

Studies excluded for not meeting inclusion

criteria

Studies identified through reference

review

30 studies with 38 unique effect sizes / samples included in meta-analysis

ID E

N T

IF IC

A T

IO SC

R E

E N

IN E

L IG

IB IL

IT Y

IN

C L

U D

E D

Fig. 1 PRISMA diagram of study identification, screening, eligibility, and inclusion process

6 Standard error (SE) estimates were calculated when not provided for bivariate effects using the for- mula: 1√ N -3, and for multivariate effects, the SE is calculated using the formula: r/(b/SE) where r is the effect size and b/SE is the ratio of the unstandardized regression coefficient to its SE (see Pratt et al., 2014).

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sample) have been published and were not included in any prior meta-analysis of this type.

Participant Type refers to whether the sample an effect size was calculated from consisted of youth (n = 4) or adult (n = 34) participants in a MHC diversion program. As no study to date has examined the overall effect of MHCs on recidivism among juvenile participants or how these effects may vary from the effect sizes observed

Table 1 Descriptive statistics on mental health courts and recidivism study samples

n = 38. Sample size range: 64–8,237, Treatment group size range: 31–1,084. One study did not include a location site for the mental health court, location effect size n = 37

Frequency Percent

Participant Type Youth 4 10.5 Adults 34 89.5

Analysis Type Bivariate 15 39.5 Multivariate 23 60.5

Study Design Within Individuals (Pre-Post) 7 18.4 Between Groups (Treat-Control) 31 81.6

Causal Effects No randomization 31 81.6 Randomization, PSM, Fixed effects 7 18.4

Follow-Up Period Less than 1 year 4 10.5 1 year 22 57.9 More than 1 year 12 31.6

Control for Criminal History

No 24 63.2 Yes 14 36.8

Control for Mental Health Diagnosis No 29 76.3 Yes 9 23.7

Control for Mental Health Services No 34 89.5 Yes 4 10.5

Control for Gender No 21 55.3 Yes 17 44.7

Control for Race/Ethnicity No 22 57.9 Yes 16 42.1

Statistical Significance p < .001 16 42.1 p < .01 7 18.4 p < .05 11 28.9 Not Significant 4 10.5

Location North 3 7.9 South 10 26.3 Midwest 7 18.4 West 17 44.7

Publication Year 2000–2004 2 5.3 2005–2009 7 18.4 2010–2014 15 39.5 2015–2019 14 36.8

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for adult participants, inclusion of a participant type moderator is both novel and relevant.

Location is a categorial item representing where the MHC was implemented (north, n = 3; south, n = 10; midwest n = 7; and west, n = 17) in order to account for potential regional differences in efficacy that may exist for courts across the United States.

Study Design was coded to indicate if the effect size was generated from a within individual (i.e. pre/post, n = 7) or between groups (i.e. treatment/control group, n = 31) study design.

Analysis Type indicates the type of analysis used to produce the effect size con- tained in this meta-analysis, and was coded as bivariate (n = 15) or multivariate (n = 23), where multivariate analyses are able to account for potential confounding factors that may correlate with the participation in MHC and/or risk of recidivism. However, in some cases bivariate models reflect the use of random assignment to MHC treatment, and no additional controls are needed in the analyses. As such, additional moderation analyses are undertaken to assess causal effect designs.

Causal Effects reflects whether a model from which an effect size is derived is able to establish causal effects through random assignment or a statistical approxi- mation of randomization (e.g. propensity score matching, fixed effects analysis), and was coded as no randomization (n = 31), or use of randomization (n = 7).

Follow-up period is the length of time that participants’ recidivism was evalu- ated, and coded as less than one year (n = 4), one year (n = 22), or more than one year (n = 12) follow-up time.

Multiple measures were coded to reflect whether each effect size was calculated after accounting for major confounders either as a control measure in a multivari- ate analysis, as a matching criteria, or through the use of randomization. Specifi- cally, criminal history refers to whether each participant’s prior criminal history (i.e. before the current MHC diversion court case) was accounted for in the effect size calculation (n = 14), as this has been shown to strongly relate to the risk of future offending (Farrington, 1987). Mental health diagnosis indicates whether the type of mental health diagnosis was considered when evaluating MHC effects across par- ticipants (n = 9), given that some forms of mental illness are more strongly related to risk of recidivism than others (Abracen et al., 2014). Mental health treatment refers to whether prior receipt of mental health treatment and/or services was accounted for in the model (n = 4), as this may enhance the effectiveness of MHC for partici- pants who already have a head start on receiving treatment for their mental health issues. Gender indicates if the study controlled for self-reported gender affiliation of participants (n = 17), particularly given the increased rate of mental health issues for women and criminal behavior for men (Gove, 1978; Rennison, 2009). Race/ ethnicity refers to whether the effect size was calculated with race and/or ethnic- ity included in the analytical model (n = 16), given the differential rates of mental illness and offending reported among people of varying races/ethnicities (Piquero, 2015; Satcher, 2001).

Finally, the statistical significance of the relationship between MHC participant and future recidivism was evaluated to consider potential publication bias, with p < 0.001 (n = 16), p < 0.01 (n = 7), p < 0.05 (n = 11), or not statistically significant

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(n = 4) coded for each effect size. To that end, a funnel plot (Fig. 3) of the included effect sizes and associated error rates was conducted to evaluate whether any biases toward publication of higher effect sizes exist within the current studies (Light & Pillemer, 1984; Sedgwick, 2013). Visual inspection of this plot indicates no evi- dence of publication bias, as there is a near even distribution above and below the mean effect size for all samples included in the analysis (Fig. 2).

Meta‑Analytic Results

Figure 3 presents the weighted effect sizes for the relationship between MHC par- ticipation and future recidivism for each sample included in this study, and Table 2 presents the weighted meta-analytic effect sizes for the full sample of effects and at each level of the moderating variables in the analysis. A continuous random-effects model is used to allow estimates of the effects to vary across studies due to differ- ences in “treatment effect” (i.e. heterogeneity in services and treatments provided

Fig. 2 Plot to test publication bias

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in each MHC program, variation in judges, etc.) and sampling error and variabil- ity. Effect sizes, standardized to odds ratios (OR) due to the dichotomous outcome (recidivism vs. no recdivisim) are weighted according to sample size, using the method recommended by Rosenthal (1984) and standardly utilized in meta-analyses (see Pratt & Cullen, 2000; Pratt et al., 2010). This weighting places a greater empha- sis on effect sizes stemming from larger samples, which are assumed to be more rep- resentative of the population of interest (Rosenthal, 1984; see also Pratt & Cullen, 2000). In this study, the sample sizes for each of the included effect sizes range from 64 to 8,237, and total participants in the MHC treatment groups across studies range from 31 to 1,084.

The cumulative weighted effect size for the relationship between MHC partici- pation and future recidivism was OR = 0.26 (95% Confidence Interval [CI]: 0.14— 0.38, n = 38). This effect size corresponds to a 74% reduction in the odds of future offending for those who participated in MHCs. To increase comparability to prior

Fig. 3 Forest plot of effect sizes for mental health courts on recidivism

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Table 2 Mental health courts and recidivism effect sizes by moderating factors

n = 38. OR = Odds Ratio. *p < .05

OR %Δ SE 95% CI n Q

Effect Size (OR) Full Sample 0.26 -74% 0.06 0.14 – 0.38 38 13.2*

Participant Type Youth 0.28 -72% 0.29 -0.28 – 0.84 4 0.7 Adults 0.26 -74% 0.06 0.14 – 0.38 34 12.6

Analysis Type Bivariate 0.37 -63% 0.14 0.10 – 0.64 15 2.2 10.3Multivariate 0.24 -76% 0.07 0.10 – 0.37 23

Study Design Within Individuals (Pre- Post)

0.30 -70% 0.20 -0.08 – 0.69 7 1.0

Between Groups (Treat- Control)

0.26 -74% 0.06 0.14 – 0.38 31 12.2

Causal Effects No randomization 0.24 -76% 0.06 0.11 – 0.37 31 9.5 Randomization, PSM, Fixed

effects 0.44 -56% 0.18 0.10 – 0.79 7 2.5

Follow-Up Period Less than 1 year 0.29 -71% 0.16 -0.03 – 0.60 4 0.5 1 year 0.24 -76% 0.08 0.09 – 0.39 22 10.6 More than 1 year 0.31 -69% 0.12 0.06 – 0.56 12 2.0

Control for Criminal History No 0.28 -72% 0.10 0.10 – 0.47 24 4.7 8.5Yes 0.25 -75% 0.08 0.10 – 0.41 14

Control for Mental Health Diagnosis

No 0.25 -75% 0.07 0.11 – 0.38 29 9.5 3.5Yes 0.32 -68% 0.13 0.07 – 0.57 9

Control for Mental Health Treatment

No 0.24 -76% 0.06 0.12 – 0.37 34 9.9 2.5Yes 0.40 -60% 0.18 0.06 – 0.75 4

Control for Gender No 0.28 -72% 0.11 0.07 – 0.50 21 3.8 9.4Yes 0.25 -75% 0.07 0.11 – 0.40 17

Control for Race/Ethnicity No 0.29 -71% 0.10 0.08 – 0.50 22 3.8 9.3Yes 0.25 -75% 0.07 0.10 – 0.40 16

Statistical Significance p < .001 0.20 -80% 0.08 0.04 – 0.35 16 6.3 0.8 0.7

p < .01 0.24 -76% 0.16 -0.07 – 0.55 7 p < .05 0.36 -64% 0.14 0.09 – 0.63 11 Not Significant 0.73 -27% 0.26 0.23 – 1.23 4 0.8

Location North 0.50 -50% 0.22 0.08 – 0.93 3 0.1 South 0.23 -77% 0.11 0.02 – 0.45 10 2.8 Midwest 0.10 -90% 0.11 -0.12 – 0.31 7 1.6 West 0.39 -61% 0.11 0.18 – 0.60 17 3.8

Publication Year 2000–2004 0.35 -65% 0.19 -0.02 – 0.72 2 0.1 2005–2009 0.33 -67% 0.16 0.02 – 0.63 7 1.2 2010–2014 0.18 -82% 0.09 -0.01 – 0.36 15 4.3 2015–2019 0.33 -67% 0.11 0.11 – 0.54 14 6.0

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meta-analyses, the weighted OR effect size is converted to Cohen’s d = -0.74, which corresponds to a moderate to strong effect across all samples in the study.

An analysis of heterogeneity was also conducted to measure the magnitude of between-study variability in the relationship between MHC participation and rate of recidivism using the Q statistic. A statistically significant Q suggest that there is considerable variability between studies, typically caused by unaccounted modera- tor variables (Hedges & Olkin, 1984). In this case, the Q statistic for the full sample of effect sizes was statistically significant, indicating heterogeneity exists within the included effect sizes that is not likely to have been caused by sampling error alone (Q = 13.2, p < . 05). Consequently, additional meta-analytic analyses for each of the moderating factors are conducted, as the strength of the effect sizes is likely to be significantly heterogeneous across these moderators.

Analyses of the mean effect size using odds ratios (OR), for the relationship between MHC participation and recidivism in the 38 effect sizes from 30 studies were conducted (see Table 2). In order to evaluate variation in effect sizes and vari- ance across moderating variables including participant type, analysis type, causal effects, study design, follow-up period, control variables accounted for, statistical significance of findings, study location, and publication year were also analyzed.

The meta-analytic results indicated similar mean effect sizes for the impact of MHCs on recidivism among youth (OR = 0.28) and adult (OR = 0.26) participants, indicating for the first time the generality of MHC efficacy across these age popula- tions. Models that included controls (i.e. multivariate analyses) yielded greater effect sizes (OR = 0.24) compared to bivariate analyses (OR = 0.37). However, results showed larger effects among studies that did not utilize random assignment to the MHC condition (OR = 0.24) versus studies that estimated causal effects through the use of random assignment or statistical approximations of randomization (e.g., PSM, fixed effects models) (OR = 0.44). More modest variation was found across studies that used a between groups (i.e. treatment and control conditions) (OR = 0.26) and within individual (pre- and post-tests) (OR = 0.30) study designs. The length of fol- low-up produced minor variations in MHC effectiveness. Specifically, studies that used one-year follow-ups of recidivism yielded the strongest effects (OR = 0.24), followed by those using less than one-year follow-ups (OR = 0.29), and follow-ups longer than one year (OR = 0.31).

Analysis of the variation in effects across models that account for relevant con- founding features indicate that accounting for prior criminal history (OR = 0.25) yielded nearly similar results compared to models that did not control for prior criminal history (OR = 0.28), whereas models that that controlled for mental health diagnoses produced a lower average effect size (OR = 0.32) than those that did not control for mental health diagnoses (OR = 0.25). A sizable difference in effects were found among studies that controlled for receipt of mental health treatment (OR = 0.40), compared to those that did not control for mental health treatment (OR = 0.24).

Among participant feature sub-group analysis, similar but slightly larger effect sizes were seen for the association between MHC participation and recidivism when models controlled for gender (OR = 0.25) and race/ethnicity (OR = 0.25) compared to the models that did not (ORs = 0.28 and 0.29, respectively).

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The largest variation in effect sizes was found across levels of statistical signifi- cance for the relationship between MHC participation and recidivism. The weakest effects were observed for findings that were not statistically significant (OR = 0.73), but increased as the level of statistical significance increased (OR at p < 0.05 = 0.36, p < 0.01 = 0.24, p < 0.001 = 0.20). Finally, a variation in effect size was observed across geographical location, with the largest effects found for MHCs in the Mid- west region of the United States (OR = 0.10), but decreased effects were found for MHCs in the south (OR = 0.23), west coast (OR = 0.39), and north (OR = 0.50). In terms of year of publication, studies published between 2010–2014 (OR = 0.18) had the strongest effect sizes followed by 2015-2019 and 2005–2009 (ORs = 0.33) and 2000–2004 (OR = 0.35). Overall, there was a modest level of variation in findings due to differences in methodological design, including study design, causal effects, follow-up period, and the inclusion of some control variables. Surprisingly, geo- graphic location of the MHCs was asspcoated with variation in effects, along with statistical significance and publication year of the MHC evaluation.

Discussion

Mental health courts and related diversion programs have been created as a response to the overwhelming number of people with mental health disorders engaged in the “revolving door” of the criminal justice system (Baillargeon et  al., 2009). Fortu- nately, MHCs are a promising remedy that serve to intervene and provide treatment for individuals in need, rather than incarcerate and further entrench those with men- tal health issues in the justice system. However, the efficacy of these diversionary courts, and the generalizability of effects across populations, methodological study features, and research designs has not been well examined. This study represents the first meta-analysis to synthesize the effects of both adult and youth MHCs on recidivism using a sample of 38 unique effect sizes from 30 peer-reviewed studies published between 1997 and 2020 in the United States. Moreover, we present the most comprehensive analysis of the invariance of MHC efficacy across a variety of sub-groups and research design features to date. As such, there are three major take- aways from this study.

First, results of this analysis indicate that mental health courts have a sizable and significant effect on future recidivism among justice-involved people with mental health issues. Specifically, results indicate that on average, the weighted effect size is OR = 0.26, suggesting a 74% reduction in the odds of future offending among those who participated in MHCs, compared to the control group or pre-test period. These results are stronger (OR converted to d = -0.74), but generally in line with prior meta-analytic research on MHCs (g = -0.54: Sarteschi et al., 2011; d = -0.20: Lowder et al., 2018), providing further support for the overall efficacy of this men- tal health diversion program. Moreover, similar mean effect sizes were found for shorter (OR = 0.29, less than 1 year) and longer (OR = 0.31, more than 1 year) fol- low-up periods, indicating a sustained impact of MHCs on recidivism over time, as suggested in prior meta-analyses on MHCs (Lowder et al., 2018). These results are particularly promising, given that this study also contains over twice as many effect

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sizes as prior meta-analyses on MHCs, is the first assessment of MHCs with juve- nile participants, all effect sizes in this analysis stem from peer-reviewed research, and represents a more recent and expanded search period (an additional 5 years of research are included, from 2015–2020).

Second, as this study represents the first meta-analytic evaluation of MHC effi- cacy among youth participants, it is notable and promising to find that the ability for MHCs to reduce recidivism appears invariant across adult (OR = 0.26, d = -0.74) and juvenile (OR = 0.28, d = -0.70) populations. This is noteworthy given that not all problem-solving courts have been equally effective among youth participants. For instance, meta-analytic research on drug courts indicates that juvenile drug courts tend to have weaker or null effects compared to adult drug courts (Mitchell et al., 2012). Luckily, this does not appear to be the case for MHCs. One explanation for this variation in efficacy by age groups for other problem-solving courts is that drug use among adolescents may be primarily peer-influenced and more difficult for criminal justice system initiatives to intervene upon than for adults, while the mechanisms underlying the ability to effectively treat mental health issues may be more uniform across adult and juvenile populations. Additionally, as suggested by Mitchell and colleagues (2012), it is possible that drug courts accept higher risk par- ticipants (in terms of risk of recidivism) in general and have less rigorous demands for juveniles than adults, potentially leading to the disparate effects on recidivism by age group. Our findings, therefore, are particularly encouraging, considering the fact that rehabilitation, treatment, and prevention efforts associated with MHCs appear to be effective in reducing youth recidivism, but also indicate the potential for related diversion programs (such as those pre-booking) to further prevent youth engagement in the criminal justice system, avoid the long-term costs of justice involvement and incarceration, and reduce other negative outcomes for those who are routinely “shuf- fled” through the criminal justice system.

Third, these findings pose significant implications for future research, given the observed variations in program efficacy by methodological rigor and research design. Specifically, studies estimating causal effects of MHCs through the use of randomized experiments or statistical approximations of randomization (e.g., PSM, fixed effects models) yielded a weaker overall effect size (OR = 0.44) compared to studies less effective at addressing the influence of spuriousness when estimating MHC efficacy (OR = 0.24). This is concerning, as it implies that when more rigor- ously evaluated, MHCs are less effective. Such a phenomenon is not uncommon in evaluative and meta-analytic research, as non-randomized designs may overestimate treatment effects if they are unable to account for critical confounding factors related to receipt of the treatment and/or outcome (Lipsey & Wilson, 2001; Weisburd et al., 2001; Wilson et al., 2000). This finding also echoes those of previous meta-analyses on MHCs, where “higher quality” studies produced weaker mean effect sizes than “lower quality” evaluations (Lowder et al., 2018; Sarteschi et al., 2011). That said, while randomized experiments increase internal validity, there is a necessary trade- off in external validity, and the ability to accurately translate findings from a sci- entific experiment to real world settings. In other words, a mixture of field studies and natural experiments with higher external validity and randomized experimental designs with high internal validity are necessary to obtain the best estimates of a

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program’s true efficacy in the field. However, in light of the relatively limited num- ber of rigorous randomized evaluations to date (n = 7) compared to the non-rand- omized designs (n = 31), more evaluations using randomization to estimate causality in MHC effects on recidivism are needed.

Additionally, this analysis indicates notable variations in mean effect sizes for studies that accounted for, through the use of statistical controls or randomization, potential confounders such as participants’ mental health diagnosis and treatment history. This suggests that studies not accounting for the severity or type of men- tal health disorder of participants may overestimate MHC benefits on recidivism, in part because this factor in itself may play a substantial role in the risk of future offending due to increased stigma and rejection from society, or suitability of MHC recommended treatment for various types of mental health concerns (Corrigan & Wassel, 2008; Hack et  al., 2020). Similarly, studies that do not account for prior mental health treatment appear to overestimate the effects of MHCs on recidivism. This may be because buy-in is a major component to mental health treatment suc- cess, and individuals who have sought treatment in the past may have increased buy-in and commitment, thereby increasing their chances of desisting with broader support and treatment made available by the MHC (Barnert et al., 2020; Thompson et al., 2020). As such, future research not undertaking randomized designs should absolutely account for these factors as statistical controls in their models.

Notably, only minor differences in mean effect sizes were observed depending upon whether the studies accounted for prior offending, gender, and race/ethnicity of the participants. This finding was surprising, but as these factors are all known to correlate with either the increased risk of experiencing mental health issues and/or justice system involvement (Sarteschi et al., 2011), they should still be controlled for in non-randomized designs.

Taken together, these findings indicate that MHCs are a promising strategy to address the so-called “revolving door” of incarceration and criminal justice involve- ment for individuals experiencing mental health disorders. In fact, those who par- ticipated in the MHC programs, on average, experienced a 74% reduction in risk of recidivism. This is particularly notable in light of the fact that most criminal justice programs tend to show lower efficacy rates, and MHCs have the ability to make a sizable impact on decarceration and treatment efforts, given the unfortunately high prevalence of mental health issues among justice-involved people.

Study Limitations and Implications

As the first meta-analysis to examine the efficacy of juvenile MHCs, the positive findings identified in this study are highly encouraging. While more research must be conducted to further increase confidence in the findings and better understand the mechanisms that underline the most (and least) successful MHC programs, a clear implication of this work is to expand MHCs to both adult and juvenile participants. This will help reduce justice-involvement for those who may be in greatest need of treatment and rehabilitation, particularly for younger people that are incredibly impacted by early involvement in the criminal justice system.

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From an empirical standpoint, this study underscores the need to strive for the highest methodological rigor and a deeper examination of why MHCs work, for whom, and under what circumstances, in order to better understand and improve these promising diversionary programs. For instance, while the mean effects of MHCs on recidivism are encouraging, as they are averages, further analysis is needed to uncover why this variation exists, and the extent to which it is a function of methodological rigor (e.g., randomized vs. non-randomized designs, inclusion of various control measures), participant features (e.g., different mental health diagnoses and needs, treatment history), or program implemention (e.g., types and quality of services and treatment provided).

To that end, while efforts were taken to assess heterogeneity in effects due to study quality, as is the case with all meta-analyses, findings still rely upon the underlying studies and potentially error in effects due to unaccounted for factors such as data quality, imprecise measurement, and shortcomings in the analyti- cal model or study design. Efforts to address these limitations include the use of effects drawn solely from peer-reviewed publications to reduce the likelihood of major errors, use of a continuous random effects meta-analytic model, and con- sideration of the influence of a multitude of methodological and study design features on the resultant effect sizes. Although potential skew towards positive and significant effects is a common concern and limitation in meta-analytic work, analyses undertaken in this study suggest that there was minimal indication of publication bias. Moreover, while analyses of the effects of MHCs for youth were nearly identical to results from adult populations, additional research is needed to assess the reliability of these findings given that limited (n = 4) effect sizes were available for inclusion in this study. Increasing our assessment of juvenile MHCs will also allow for youth-specific moderation analyses to be conducted, and potentially isolate the most effective components for use in future youth diversion programs around the nation.

Finally, while considerable efforts were made to identify sources of variation in MHC effectiveness, unfortunately, data were not available to evaluate the quality and appropriateness of services provided by MHCs for this analysis. As little research has examined the quality of community mental health resources, case management, and psychiatric services made available to justice-involved people participating in MHCs (Boothroyd et al., 2005; Erickson et al., 2006; Perlin, 2003), it is critical that these components be evaluated as a potential source of variation in effects, and/or as models of success. Future research can help address this limitation by reporting and analyzing specifics aspects of MHC implementation, as this is both vital to the broader success of MHCs and may relate to heterogenic treatment effects across subgroups of the population (Erickson et al., 2006; Perlin, 2003). This is vital for policymakers and practitioners to expand MHCs as a diversion option, and ensure the “active ingredients” of MHCs are enriched to improve the program’s positive effects across a variety of participants and contexts.

In sum, findings from this study underscore for policymakers the value of imple- menting MHCs for both adult and juvenile populations, and the concurrent need for rigorous evaluations to unpack the facets of MHC programming and increase the efficacy of MHCs for a broader array of participants.

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Conclusion

This study set out to examine the overall effectiveness of MHCs on the reduc- tion of recidivism among justice-involved people with mental health issues, and whether these findings generalize across participants (particularly youth) or vary by methodological design and study features. Results from this meta-analysis, which draw upon the most contemporary and highest quality research available to date, suggest that MHCs correspond to a sizable reduction in risk of recidivism among participants, and these results generalize across adult and juvenile popula- tions, and across many methodological and study design features. While there are many avenues for future research, this study provides considerable evidence that MHCs are a positive and effective alternative to incarceration for the substan- tial number of people in the justice-system who experience mental health issues, and a viable means of diverting those in need away from the penal system and towards treatment, services, and opportunities for a brighter future.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Bryanna Fox is an associate professor in the Department of Criminology and co-director of the Center for Justice Research & Policy at the University of South Florida. She earned her Ph.D. from the University of Cambridge and is a former FBI Special Agent. Her research focuses on the developmental and psycho- logical risk factors for offending across the life-course, and the development and evaluation of evidence- based policing and crime prevention strategies.

Lauren N. Miley is a doctoral candidate in the Department of Criminology at the University of South Florida. She is currently the lead research supervisor  in the SPRUCE lab. Her research interests include criminal justice policy, mental health in the criminal justice system, and developmental and life-course criminology.

Kelly E. Kortright is a doctoral student in the Department of Criminology, and a research supervisor in the SPRUCE lab at the University of South Florida. She earned her M.S. in Criminal Justice from the Univer- sity of Alabama. Her research interests include developmental and life-course criminology, strain theory, risk factors for offending, and mental health in the criminal justice system.

Rachelle J. Wetsman holds a B.A. in Psychology from the University of South Florida, and is currently pursuing an M.A. in Criminal Justice from the John Jay College of Criminal Justice. Her research inter- ests include crime etiology, mental health and crime, and the risk-need-responsivity framework.

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