Highlights
- •Number of individual counseling sessions positively predicts drug court graduation.
- •Emotional risk factors are associated with lower likelihood of graduation.
- •Individuals with more monetary fine and jail sanctions are less likely to graduate.
Abstract
Introduction
Although existing research suggests drug courts reduce recidivism and substance use, a large portion of drug court participants do not graduate. According to a conceptual framework, severity of need and program intensity may help to explain variation in drug court effectiveness. Understanding variation in drug court graduation can help to identify high risk participants and effective programmatic elements.
Methods
Our sample included 247 drug court participants from an adult felony-level drug court located in a large metropolitan area of the southeastern United States that either graduated (n = 113) or were terminated (n = 134) from the program. We used participant and program characteristics from drug court program records to predict drug court graduation.
Results
In bivariate analyses, several participant and program characteristics were significantly associated with drug court graduation. In the final multivariate model, only one participant-level characteristic was significantly related to graduation: emotional/personal risk and needs (aOR: 0.56, 95% CI: 0.33, 0.93). Alternatively, three program characteristics remained statistically significant predictors of drug court graduation in the final multivariate model. Receiving more individual counseling sessions was positively associated with drug court graduation (aOR: 1.27, 95% CI: 1.14, 1.41), while jail and monetary fine sanctions were negatively associated with drug court graduation (aOR jail: 0.45, 95% CI: 0.30, 0.68; aOR fine: 0.28, 95% CI: 0.10, 0.78).
Conclusions
Our findings suggest that drug court programs may benefit by tailoring services for individuals with high emotional/personal risk and participants who receive certain types of sanctions. More rigorous research should explore the causal relationship between individual counseling and drug court graduation to determine if wide-scale programmatic changes are warranted.
Keywords
1. Introduction
More than 1.5 million incarcerated individuals in the United States' criminal justice system have a substance use disorder (SUD), but only a small portion receive SUD treatment before, during, or after incarceration (
Bollinger et al., 2016
). Since the 1990s, drug courts helped to address this SUD treatment gap and reduce crime simultaneously (Lurigio, 2008
). A large body of evidence suggests that drug courts reduce recidivism (Mitchell et al., 2012
). Some studies point to reductions in drug use, as well (Gottfredson et al., 2005
). However, the effectiveness of drug court varies significantly at the individual level. In fact, approximately 30–50% of drug court participants do not complete drug court (Dematteo et al., 2009
). Understanding individual-level variation in drug court graduation can help to identify high-risk participants, highlight the most effective programmatic elements, and tailor services by risk level to increase graduation rates.Longshore et al., 2001
identified a conceptual framework to examine variation in drug court effectiveness. The framework included two overarching categories: 1) drug court structural characteristics and 2) drug court process characteristics (Longshore et al., 2001
). One domain of structural characteristics is severity of need (i.e., population severity), which describes the characteristics of the drug court participants. Longshore et al., 2001
hypothesized that severity of need has an inverse relationship with drug court outcomes. However, the authors also speculated that program intensity (a domain of process characteristics) could moderate the relationship between severity of need and drug court outcomes, with program intensity positively related to positive outcomes. Program characteristics include variables such as urine testing, court appearances, and substance use treatment requirements. Although many studies examined individual domains (e.g., severity of need or program intensity), studies examining both domains are less common.Previous research suggests that severity of need is related to graduation rates. Ample research suggests that graduation is more likely among clients who are older (
Hickert et al., 2009
; Mateyoke-Scrivner et al., 2004
; Stageberg et al., 2001
; Wolf et al., 2003
; DeVall and Lanier, 2012
), female (Gallagher et al., 2020
; Gray and Saum, 2005
; Hartman et al., 2007
; Stageberg et al., 2001
), White vs. non-White (Brewster, 2001
; Butzin et al., 2002
; Fulkerson et al., 2012
; - Fulkerson A.
- Keena L.D.
- O'Brien E.
Understanding success and nonsuccess in drug court.
The International Journal of Comparative Criminology. 2012; 57: 1297-1316https://doi.org/10.1177/0306624X12447774
Gray and Saum, 2005
; Hartley and Phillips, 2001
; Ho et al., 2018
; Mateyoke-Scrivner et al., 2004
; Shah et al., 2013
; Shannon et al., 2016
; Stageberg et al., 2001
), employed (Butzin et al., 2002
; Gallagher et al., 2020
; Hartley and Phillips, 2001
; Listwan et al., 2009
; Mateyoke-Scrivner et al., 2004
; Roll et al., 2005
; Stageberg et al., 2001
; Wu et al., 2012
), and more educated (Gill, 2016
; Gray and Saum, 2005
; Listwan et al., 2009
; Mateyoke-Scrivner et al., 2004
; Shah et al., 2013
; Shannon et al., 2016
; Stageberg et al., 2001
). Multiple studies also suggest that individuals with more serious criminal histories (Gallagher et al., 2020
; Gray and Saum, 2005
; Mateyoke-Scrivner et al., 2004
; Miller and Shutt, 2001
; Peters et al., 1999
; Rempel and DeStefano, 2001
; Shannon et al., 2016
; Wolf et al., 2003
) and those who use harder drugs (e.g., cocaine, opioids) are less likely to graduate (Brown, 2010a
; Hartley and Phillips, 2001
; Hickert et al., 2009
; Mateyoke-Scrivner et al., 2004
; Wolf et al., 2003
). A few studies also found that co-occurring mental health problems (Mendoza et al., 2013
; Shannon et al., 2016
) and SUD severity (Butzin et al., 2002
; Gray and Saum, 2005
; Shah et al., 2013
) were associated with lower drug court graduation rates.However, criticisms of drug court studies include focusing on participant factors, while largely ignoring programmatic factors (e.g., sanctions, treatment sessions). Programmatic factors are more difficult to collect and analyze but are worth examining because they can be changed, and even tailored to individuals, while most participant-level risk factors cannot be. For example, in the few studies that examined sanctions, receiving sanctions was associated with lower odds of graduation (
Goldkamp et al., 2001
; Shannon et al., 2016
). A meta-analysis of 76 drug courts found that increased program requirements predicted drug court effectiveness to a moderate degree, although some specific program requirements (e.g., community service sanctions, fines) were negatively associated with effectiveness (Shaffer, 2011
). However, these studies assessed program requirements (i.e., expectations) rather than actual treatment received (e.g., number of counseling sessions), which can differ significantly in practice.Additionally, drug courts admit primarily lower risk individuals (e.g., lower SUD severity, limited prior arrest history), which may falsely skew overall graduation rates upward (
King and Pasquarella, 2009, April
; Belenko et al., 2011
). Thus, populations that are most in need of drug court intervention do not receive enough research attention. Relatedly, some expert groups call for drug court research using higher risk samples, and more diverse samples, particularly including more Black participants (Gallagher et al., 2020
). Finally, the study of many important participant risk factors has been limited with prior studies often neglecting factors such as SUD severity, co-occurring mental health problems, social support, and socioeconomic status.The primary goal of this study was to examine predictors of drug court graduation, including both participant and programmatic factors. Our approach used both bivariate and multivariate logistic regression with a diverse sample from an adult felony-level drug court located in a large metropolitan area of the southeastern United States. The current study drew from a drug court that serves both moderate- and high-risk clients, allowing us to examine a full range of baseline risk variation. Drawing on past literature, we hypothesized that gender, race, age, education, employment, drug of choice, and criminal history would be significant predictors of drug court graduation in bivariate and multivariate analyses. We also hypothesized that one or more programmatic factors would significantly predict graduation in bivariate and multivariate analyses, because, theoretically, increased program intensity should lead to increased likelihood of graduation (
Longshore et al., 2001
).2. Material and methods
2.1 Setting
The setting for this study was a post-adjudication adult felony-level drug court located in a large metropolitan area of the southeastern United States in operation since the early 2000s. The court had two treatment tracks based on clients' initial risk and need level. Track 1 included individuals at higher risk for recidivism with severe substance use disorders and involved five treatment phases over 24 months. Track 2 included individuals with moderate-to-lower risk and needs who presented with protective factors (e.g., stable employment history, stable residence, fewer felony convictions) and involved four treatment phases over 21 months. Each program phase included a minimum number of treatment sessions and court status hearings. For example, when Track 1 participants moved to phase three (approximately month nine in the program), they attended bi-monthly court status hearings, monthly individual treatment and case management sessions, and group treatment sessions twice a week for a minimum of 90 days. Participants' individualized treatment plans could specify more individual, group, case management, or court sessions on an individual basis. Drug screening was frequent, randomized, supervised and occurred seven days a week. The program tested participants two to three times per week in early phases and decreased testing as participants progressed toward program completion. Due to differences in treatment expectations for Track 1 and Track 2 participants, we examined whether track moderated the predictors of graduation.
Track 1 began with 18 months of intensive treatment (phases one through four) and was often paired with recovery residence housing. Track 1 started with more than 20 h of care each week, following the placement criteria set by the American Society of Addiction Medicine (ASAM; 2.5 Level of Care). The manualized curricula and services included a focus on criminal and addictive thinking, relapse-prevention, and education programming, life skills, employment, and community supervision. Track 1 ended with six months of less intensive treatment and recovery maintenance (phase five). Track 2 started with nine months of intensive outpatient services (ASAM Level of Care 2.1), including manualized curricula for relapse-prevention, criminal and addictive thinking, and education programming, life skills, and community supervision (phases one through three). Track 2 concluded with 12 months of more limited treatment requirements and recovery maintenance (phase four). Graduation criteria for all program participants included progressing through all program phases, no positive drug screens for six months before graduation, no sanctions for 60 days before graduation, completing an aftercare plan, and an exit LSI-R assessment.
Clients had to take part in all drug court programming, and clients received sanctions for missing drug court activities (e.g., counseling sessions, court). The drug court implemented sanctions for a number of other infractions, including positive drug screens, violent/disruptive behavior, or rearrest. This program utilized six different types of sanctions: treatment response, community service, jail, two-week behavior contract, 30-day step up, and monetary fines. The drug court team collaboratively created a sanctions matrix to define problem behaviors and subsequent responses (
Guastaferro and Daigle, 2012
). Each track used participant handbooks, which included expectations, policies, and possible sanctions. While the court strived for consistency in sanction implementation, the team considered contextual factors when determining sanctions.2.2 Sample and procedures
The data came from two primary sources. Most of the data originated from drug court program records, which drug court staff collected as part of routine pre-enrollment assessment, and program tracking and delivery. Before admission to the program, participants took two assessments to determine their level of risk and need: 1) the Level of Service Inventory-Revised (LSI-R;
Andrews and Bonta, 2000
) and 2) the Texas Christian University Drug Screen or TCUDS (Institute of Behavioral Research, 2020
). Drug court staff collected data on days in program, sessions attended (individual, group, case management and court), and sanctions as the participant progressed through the program.The study obtained arrest data for clients enrolled in the drug court program from state records. These data included arrests and charges that occurred in the state (and thus, these data did not capture any arrests/charges occurring outside of the state).
2.3 Measures
2.3.1 Drug court graduation
The primary outcome for this study was graduation from drug court. Drug court program records reported participant status as one of the following: AWOL, active in program, administrative discharge, declined, graduated, in custody, inactive in program, pending termination, or terminated. We created a binary graduation variable, where graduation equaled 0 if the participant's status was administrative discharge, pending termination, or terminated; and graduation equaled 1 if the participant graduated from the program. This analysis excluded all other status types.
2.3.2 Participant risk and need measures
The program placed participants in either Track 1 (high risk) or Track 2 (moderate risk), based on an initial risk-need assessment by clinical directors of each track and drug court team agreement.
2.3.2.1 Demographic characteristics
Demographic characteristics included gender; race/ethnicity; age, and marital, educational, and employment status, collected during the pre-enrollment assessment. Drug court program records classified gender as male or female. Because the majority of the sample was Black and the sample size for other groups was limited, we dichotomized race/ethnicity as Black versus non-Black (including White, Hispanic, Asian, and mixed). The LSI-R assessment administered at enrollment assessed participant age, marital status, educational status, and employment status. The marital status variable categorized participants as married (including common law, married, or living as married) or not married (including divorced, separated, single, or widowed). The educational status variable dichotomized participants into those who graduated from high school or a higher degree (e.g., AA) and those who did not graduate from high school (including those who later earned a GED, unless they pursued further education). For employment status, the LSI-R assessment deemed participants unemployed unless they met the following criteria: worked 30+ hours per week, full-time student, received disability compensation or pension, or full-time care-taker.
2.3.2.2 Criminogenic risk and needs
Prior to enrollment, participants completed the Level of Service Inventory-Revised (LSI-R), which drug court staff used to guide treatment strategies (
Guastaferro and Daigle, 2012
). Effective justice system interventions consider an individual's risk for recidivism and address specific criminogenic needs. Primary criminogenic needs are dynamic (i.e., can change); have a robust and direct relationship with criminal behavior; and center on antisocial personality patterns, antisocial cognitions and associates, negative social supports, and use of criminogenic drugs (cocaine/crack cocaine, heroin/other opiates, and methamphetamines/other amphetamines) (Andrews and Bonta, 2010
; Taxman et al., 2013
). Primary criminogenic needs include substance use for individuals with SUDs involving substances with an empirically based and direct relationship with criminal behavior (Bennett et al., 2008
; Pierce et al., 2015
; Taxman et al., 2013
). Marijuana and alcohol use may merit intervention but do not have a direct relationship with criminal behavior (with the exception of DUI; see Taxman et al., 2013
). Secondary dynamic needs, whose relationship with criminal behavior is indirect, include education and employment, mental health, and housing. Individuals assessed with multiple criminogenic needs should be prioritized for intensive treatment services (Taxman et al., 2013
).Drug court staff administered the LSI-R via a semi-structured interview, with 54 items scored as 0/1 or on a 0–3 scale. Each item weighed one point with a maximum total score of 54, and higher scores indicated greater risk/needs. The assessment included ten subscales: criminal history (ten items on rule-breaking and involvement with the criminal legal system); education/employment (ten items on skills, work stability, and structure); financial (two items on money and opportunity for material success); family/marital (four items on social support); accommodation (three items on domestic stability); leisure/recreation (two items on use of free time); companions (five items on social values and influences); alcohol/drug problems (nine items on frequency/intensity of use, adverse consequences, and readiness for change); emotional/personal (five items on mental health issues, emotional management, and anti-social personality features); and attitude/orientation (four items on norms and pro-social/criminal activities).
2.3.2.3 Substance use characteristics
Substance use characteristics included drug of choice (DOC) and polydrug use. Using data from the TCUDS assessment, we dichotomized DOC into criminogenic drugs (cocaine/crack cocaine, heroin/other opiates, and methamphetamines/other amphetamines) versus non–criminogenic drugs (alcohol, marijuana, and other). Polydrug use indicated use of two or more drugs (monthly or more frequently).
2.3.2.4 Arrest history
Using state arrest data, we created several variables representing the number of arrests that occurred prior to the client's enrollment in drug court: total arrests, felony arrests, violent arrests, and drug-related arrests.
2.3.3 Program measures
Program variables included days in program, counseling sessions (group and individual), court sessions, case management sessions, and sanctions. Days in program equaled the enrollment date minus the end date in drug court program records. Individual counseling, group counseling, court, and case management variables summed sessions attended for each individual. Sanction variables summed the following sanction types for each individual: treatment response, community service, jail, behavior contract, 30-day step up, and monetary fines.
2.4 Analytic plan
Statistical analyses used SAS® statistical software. First, descriptive statistics (means, frequencies) described the sample and all variables. Additionally, chi-square and t-tests tested differences between Track 1 and Track 2 participants. Next, bivariate analyses (chi-square, t-tests) tested differences between graduates and non-graduates, and we presented odds ratios. Due to observed differences in participant and program characteristics between Track 1 and Track 2 participants, we used regression analyses with interaction terms between track and each predictor to test whether predictors of graduation differed between Track 1 and Track 2 participants. Then, we created two multivariate logistic regression models based on two conceptual groupings of variables (participant risk variables, program characteristics), including any variable with p < 0.20 in the bivariate analyses. A final multivariate model included variables from both conceptual groupings with p < 0.20 in the prior two multivariate logistic regression models.
3. Results
3.1 Participants
Among 247 drug court participants in our sample, the majority were male, Black, and at the time of enrollment, unemployed and not married, with an average age of 39.3 years (Table 1). Just over half of participants graduated high school. Four-fifths of participants indicated a criminogenic DOC, with cocaine the most common DOC (54%). Participants entered drug court with an average of 17.2 prior total arrests and 10.1 prior felony arrests, but fewer than one arrest involving violence. Participants' average total LSI-R Risk-Needs Assessment Score was 25.3, indicating moderate-to-high risk for a community-based program. Individuals with an LSI-R score of 25 have a 40% chance of recidivating within one year, without any intervention (
Andrews and Bonta, 2000
).Table 1Drug court participant characteristics, overall and by track (N = 247).
| Overall (N = 247) | Track 1 (N = 134) | Track 2 (N = 113) | p-Value | |
|---|---|---|---|---|
| Participant risk variables | ||||
| % Track 1 (higher risk track) | 54.2 | – | – | – |
| Demographic characteristics | ||||
| % male | 77.7 | 73.1 | 83.2 | 0.059 |
| % Black | 61.8 | 69.9 | 52.2 | 0.004 |
| Age, M (SD) | 38.9 (11.2) | 41.6 (10.8) | 35.8 (10.8) | <0.001 |
| % married | 10.8 | 9.1 | 12.8 | 0.350 |
| % graduated high school | 53.8 | 40.4 | 69.8 | <0.001 |
| % employed | 38.4 | 19.8 | 60.9 | <0.001 |
| Substance use characteristics | ||||
| % cocaine drug of choice | 55.4 | 65.7 | 42.4 | 0.001 |
| % criminogenic drug of choice | 81.9 | 88.0 | 74.1 | 0.013 |
| % polydrug use | 50.9 | 57.0 | 43.9 | 0.056 |
| Arrest history | ||||
| Total arrests, M (SD) | 17.2 (14.2) | 22.1 (15.4) | 11.6 (10.2) | <0.001 |
| Felony arrests, M (SD) | 9.9 (9.4) | 13.0 (10.3) | 6.5 (6.8) | <0.001 |
| Violent arrests, M (SD) | 0.7 (1.2) | 0.8 (1.3) | 0.6 (1.0) | 0.172 |
| Drug-related arrests, M (SD) | 2.3 (2.3) | 2.4 (2.5) | 2.2 (2.1) | 0.478 |
| LSI-R risk-needs assessment | ||||
| LSI-R total score, M (SD) | 24.9 (8.5) | 29.3 (7.0) | 19.7 (7.0) | <0.001 |
| Criminal history | 4.6 (2.0) | 5.2 (1.6) | 3.9 (2.2) | <0.001 |
| Education/employment | 4.2 (2.7) | 5.6 (2.4) | 2.6 (2.1) | <0.001 |
| Financial | 1.1 (0.7) | 1.3 (0.6) | 0.8 (0.7) | <0.001 |
| Family/marital | 1.9 (1.3) | 2.4 (1.3) | 1.4 (1.2) | <0.001 |
| Accommodations | 1.0 (1.1) | 1.5 (1.1) | 0.4 (0.7) | <0.001 |
| Leisure/recreation | 1.5 (0.8) | 1.6 (0.7) | 1.3 (0.9) | <0.001 |
| Companions | 2.5 (1.4) | 2.9 (1.4) | 2.0 (1.2) | <0.001 |
| Alcohol/drugs | 6.5 (2.1) | 7.0 (1.8) | 5.9 (2.3) | <0.001 |
| Emotional/personal | 1.6 (1.4) | 1.6 (1.5) | 1.6 (1.4) | 0.778 |
| Attitudes/orientations | 0.4 (0.8) | 0.6 (0.9) | 0.1 (0.3) | <0.001 |
| Program variables | ||||
| % graduated drug court | 45.8 | 38.1 | 54.9 | 0.008 |
| Days in program, M (SD) | 632.8 (327.3) | 596.5 (343.4) | 676.3 (302.8) | 0.061 |
| Sessions | ||||
| Individual sessions, M (SD) | 20.2 (11.5) | 18.2 (12.3) | 22.5 (10.2) | 0.004 |
| Group sessions, M (SD) | 151.1 (98.8) | 180.8 (114.3) | 116.1 (60.6) | <0.001 |
| Case mgmt. sessions, M (SD) | 16.0 (12.5) | 10.6 (8.9) | 21.5 (13.2) | <0.001 |
| Court sessions, M (SD) | 65.7 (33.9) | 72.9 (38.6) | 57.2 (24.9) | <0.001 |
| Sanctions | ||||
| % received any sanction | 83.4 | 85.1 | 81.4 | 0.441 |
| % received treatment sanction | 5.3 | 5.2 | 5.3 | 0.976 |
| % received community service sanction | 56.3 | 60.5 | 51.3 | 0.150 |
| % received jail sanction | 74.5 | 76.9 | 71.7 | 0.352 |
| % received behavior contract sanction | 9.7 | 7.5 | 12.4 | 0.193 |
| % received 30-day step up sanction | 32.8 | 20.9 | 46.9 | <0.001 |
| % received monetary fine sanction | 18.2 | 17.9 | 18.6 | 0.891 |
Participant risk characteristics varied for Track 1 and Track 2 participants. Track 1 (higher risk track) participants were significantly more likely to be Black, older, and unemployed than Track 2 participants. Track 1 participants were also significantly less likely to have graduated from high school, but more likely to report a criminogenic drug as their DOC, than Track 2 participants. On average, Track 1 participants had more total and felony arrests prior to drug court enrollment. Track 1 participants also scored significantly higher on all LSI-R Risk-Needs Assessment subscales, except for the emotional/personal subscale.
In this program, 46% of participants graduated from drug court and 54% did not. On average, participants attended 18.5 individual, 146.6 group, 16.2 case management, and 60.6 court sessions during their time in the program. While in drug court, 79% of participants received one or more sanctions and 70% received a jail sanction. Program variables also varied for Track 1 and Track 2 participants. Track 1 (higher risk track) participants were less likely to graduate from drug court (38% vs. 55%, p = 0.008). Track 1 participants attended significantly fewer individual and case management sessions, but significantly more group and court sessions, than Track 2 participants.
3.2 Bivariate analyses: Participant risk variables
3.2.1 Demographic and substance use characteristics
Bivariate analyses (Table 2) showed that older drug court participants were significantly more likely to graduate than their younger counterparts. Specifically, drug court graduates averaged 42.3 years of age, while non-graduates averaged 35.4 years (OR = 1.062, CI: 1.033, 1.091). Additionally, participants who indicated a criminogenic DOC were more likely to graduate than those who indicated a non-criminogenic DOC (51% vs. 31%, OR = 2.30). Note that Table 2 lists specific drug types for descriptive purposes only. Track 2 clients (moderate-to-low risk) were more likely to graduate than higher risk Track 1 clients (55% vs. 38%, OR = 1.98). Graduation rates did not differ by gender, race, marital status, education, employment, or polydrug use.
Table 2Demographic and substance use characteristics of drug court graduates and non-graduates (N = 247).
| n of grads N = 113 | n of non grads N = 134 | % grad | OR | 95% CI | p-Value | |
|---|---|---|---|---|---|---|
| Demographic characteristics | ||||||
| Sex | ||||||
| Female | 23 | 32 | 42 | Ref | ||
| Male | 90 | 102 | 47 | 1.228 | 0.670, 2.251 | 0.5073 |
| Race | ||||||
| Black | 67 | 85 | 44 | Ref | ||
| Non-Black | 48 | 46 | 49 | 1.216 | 0.726, 2.036 | 0.4578 |
| Age (mean, SD) | 42.3, 9.9 | 35.4, 11.4 | – | 1.062 | 1.033, 1.091 | <0.0001 |
| Marital status | ||||||
| Not married | 97 | 118 | 45 | Ref | ||
| Married | 13 | 13 | 50 | 1.216 | 0.539, 2.746 | 0.6372 |
| Education at enrollment | ||||||
| Did not graduate HS | 43 | 54 | 44 | Ref | ||
| Graduated HS | 63 | 50 | 56 | 1.582 | 0.916, 2.732 | 0.0996 |
| Employment at enrollment | ||||||
| Unemployed | 57 | 68 | 46 | Ref | ||
| Employed | 46 | 32 | 59 | 1.715 | 0.968, 3.039 | 0.0647 |
| Substance use characteristics | ||||||
| Drug of choice (DOC) | ||||||
| DOC is “criminogenic” | 81 | 77 | 51 | 2.295 | 1.053, 5.000 | 0.0366 |
| Cocaine/crack cocaine | 57 | 50 | 53 | – | – | – |
| Heroin/other opiates | 12 | 16 | 43 | – | – | – |
| Methamphetamines/other amphetamines | 12 | 11 | 52 | – | – | – |
| DOC is not “criminogenic” | 11 | 24 | 31 | Ref | ||
| Alcohol | 2 | 10 | 17 | – | – | – |
| Marijuana | 8 | 10 | 44 | – | – | – |
| Other | 1 | 4 | 20 | – | – | – |
| Polydrug use | ||||||
| Polydrug use | 54 | 54 | 50 | Ref | ||
| Single drug use | 54 | 50 | 52 | 1.080 | 0.630, 1.851 | 0.7795 |
| Track | ||||||
| Track 1 (higher risk) | 51 | 83 | 38 | Ref | ||
| Track 2 (lower risk) | 62 | 51 | 55 | 1.978 | 1.189, 3.291 | 0.0086 |
Note: Measures have various missing values between 0 and 54 (21.9% of total sample).
3.2.2 Arrest history and LSI-R risk-needs assessment
Non-graduates entered drug court with nominally more total, felony, and violent arrests, but the differences were not statistically significant (Table 3). Participants with lower total scores on the LSI-R risk-needs assessment (i.e., lower risk) were more likely to graduate (OR: 1.055, CI: 1.020, 1.092). Graduates scored significantly lower in the education/employment (OR: 0.89, CI: 0.81, 0.99), accommodations (OR: 0.75, CI: 0.58, 0.98), leisure/recreation (OR: 0.51, CI: 0.35, 0.75), companions (OR: 0.75, CI: 0.61, 0.92), alcohol/drugs (OR: 0.83, CI: 0.73, 0.96), and emotional/personal (OR: 0.73, CI: 0.60, 0.89) LSI-R subscales, indicating lower risk in each of those areas for graduates versus non-graduates.
Table 3Arrest history and risk-needs assessment scores for graduates and non-graduates.
| Graduates (N = 113) | Non-graduates (N = 134) | OR | 95% CI | p-Value | |||
|---|---|---|---|---|---|---|---|
| M | SD | M | SD | ||||
| Arrest historya | |||||||
| Total arrests | 16.61 | 13.40 | 17.76 | 14.91 | 0.994 | 0.976, 1.013 | 0.5393 |
| Felony arrests | 9.70 | 8.44 | 10.17 | 10.18 | 0.995 | 0.967, 1.023 | 0.7004 |
| Violent arrests | 0.72 | 1.13 | 0.77 | 1.26 | 0.962 | 0.774, 1.196 | 0.7277 |
| Drug-related arrests | 2.43 | 2.26 | 2.25 | 2.36 | 1.034 | 0.924, 1.157 | 0.5603 |
| Risk-needs assessment | |||||||
| LSI-R total score (54 items) | 23.06 | 9.27 | 26.74 | 7.14 | 0.948 | 0.916, 0.980 | 0.0020 |
| Criminal history (10) | 4.49 | 2.03 | 4.70 | 2.00 | 0.949 | 0.829, 1.085 | 0.4423 |
| Education/employment (10) | 3.79 | 2.68 | 4.59 | 2.65 | 0.893 | 0.806, 0.990 | 0.0313 |
| Financial (2) | 0.96 | 0.68 | 1.14 | 0.66 | 0.668 | 0.444, 1.004 | 0.0520 |
| Family/marital (4) | 1.84 | 1.35 | 1.98 | 1.34 | 0.926 | 0.757, 1.132 | 0.4540 |
| Accommodations (3) | 0.83 | 1.03 | 1.15 | 1.10 | 0.754 | 0.583, 0.977 | 0.0324 |
| Leisure/recreation (2) | 1.29 | 0.87 | 1.66 | 0.62 | 0.514 | 0.352, 0.750 | 0.0006 |
| Companions (5) | 2.25 | 1.39 | 2.78 | 1.33 | 0.752 | 0.613, 0.922 | 0.0062 |
| Alcohol/drugs (9) | 6.14 | 2.45 | 6.91 | 1.58 | 0.833 | 0.727, 0.955 | 0.0089 |
| Emotional/personal (5) | 1.32 | 1.34 | 1.93 | 1.43 | 0.730 | 0.598, 0.892 | 0.0020 |
| Attitudes/orientations (4) | 0.37 | 0.84 | 0.36 | 0.65 | 1.026 | 0.716, 1.471 | 0.8873 |
Note: LSI-R = Level of Service Inventory-Revised (higher scores = higher risk).
a Arrests before enrollment.
3.3 Bivariate analyses: program variables
On average, graduates spent 785 days in the program, while non-graduates spent an average of 494 days before being terminated or discharged. Due to this difference and because one naturally receives more sessions when in the program, analyses controlled for the number of days in the program when comparing treatment sessions and sanctions among graduates and non-graduates (Table 4).
Table 4Program characteristics among graduates and non-graduates.
| Graduates (N = 113) | Non-Graduates (N = 134) | OR | 95% CI | p-Value | |||
|---|---|---|---|---|---|---|---|
| M | SD | M | SD | ||||
| Days in program | 785.00 | 152.06 | 494.18 | 379.24 | 1.004 | 1.003, 1.005 | <0.0001 |
| Treatment sessions | aOR | ||||||
| Total sessions | 236.12 | 80.67 | 173.82 | 117.22 | 1.002 | 0.998, 1.006 | 0.3175 |
| Individual counseling | 27.15 | 5.69 | 13.45 | 11.75 | 1.195 | 1.121, 1.274 | <0.0001 |
| Group counseling | 189.61 | 78.18 | 118.36 | 102.85 | 1.004 | 1.000, 1.008 | 0.0429 |
| Case management | 19.36 | 10.04 | 12.53 | 13.78 | 1.024 | 0.992, 1.056 | 0.1402 |
| Court | 68.50 | 21.86 | 63.38 | 41.39 | 0.990 | 0.980, 1.001 | 0.0715 |
| Sanctions | aOR | ||||||
| Total sanctions | 5.32 | 5.15 | 5.63 | 6.22 | 0.851 | 0.795, 0.911 | <0.0001 |
| Treatment response | 0.09 | 0.37 | 0.04 | 0.21 | 0.966 | 0.339, 2.751 | 0.9487 |
| Community service | 2.01 | 2.55 | 1.65 | 2.47 | 0.838 | 0.731, 0.961 | 0.0112 |
| Jail | 2.26 | 2.33 | 2.87 | 2.80 | 0.680 | 0.586, 0.790 | <0.0001 |
| Behavior contract | 0.05 | 0.23 | 0.17 | 0.51 | 0.089 | 0.031, 0.256 | <0.0001 |
| 30-day step up | 0.73 | 1.16 | 0.57 | 1.22 | 0.919 | 0.708, 1.192 | 0.5233 |
| Monetary fine | 0.19 | 0.49 | 0.33 | 0.75 | 0.339 | 0.190, 0.605 | <0.0001 |
a aOR controlling for days in program.
Controlling for number of days in the program, graduates attended significantly more individual (aOR: 1.20, CI: 1.12, 1.27) and group (aOR: 1.004, CI: 1.000, 1.008) counseling sessions. Participants who received more total sanctions were less likely to graduate (aOR: 0.85, CI: 0.80, 0.91). Graduates received significantly fewer jail sanctions (aOR: 0.68, CI: 0.59, 0.79), behavior contract sanctions (aOR: 0.09, CI: 0.03, 0.26), and monetary fine sanctions (aOR: 0.34, CI: 0.19, 0.61) than non-graduates after controlling for number of days in the program.
3.4 Interaction effects by track
Only two out of 36 predictors of graduation significantly differed between Track 1 and Track 2: number of community service sanctions and LSI-R financial subscale score. Non-statistically significant findings for 34 of 36 interactions suggest that predictors of graduation were not different for Track 1 and Track 2 participants. Thus, the study combined all participants in the multivariate regression analyses.
3.5 Multivariate analyses
3.5.1 Multivariate model 1: participant variables only
Bivariate analyses generated p-values under 0.20 for ten participant risk variables; thus, multivariate model 1 included age; criminogenic DOC; track; and the following LSI-R subscales: education/employment, financial, accommodations, leisure/recreation, companions, alcohol/drugs, and emotional/personal. Only age and track statistically significantly (p < 0.05) related to graduation in this first multivariate model (Table 5, first column). Five variables maintained p-values under 0.20 in the adjusted model. Therefore, the final multivariate model included the following five participant risk variables: age, criminogenic DOC, LSI-R alcohol/drugs subscale, LSI-R emotional/personal subscale, and track.
Table 5Multivariate models.
| Variable | Model 1: Participant only | Model 2: Program only | Model 3: Final model | |||
|---|---|---|---|---|---|---|
| aOR | 95% CI | aOR | 95% CI | aOR | 95% CI | |
| Age | 1.079 | 1.039, 1.121 | 1.024 | 0.960, 1.093 | ||
| Drug of choice (DOC) | ||||||
| Criminogenic DOC | 1.832 | 0.768, 4.366 | 0.779 | 0.130, 4.687 | ||
| Non-criminogenic DOC | Ref | Ref | ||||
| LSI-R sub-scales | ||||||
| Education/employment | 1.046 | 0.894, 1.223 | ||||
| Financial | 0.732 | 0.416, 1.286 | ||||
| Accommodations | 0.900 | 0.640, 1.267 | ||||
| Leisure/recreation | 0.741 | 0.440, 1.247 | ||||
| Companions | 0.970 | 0.728, 1.292 | ||||
| Alcohol/drugs | 1.188 | 0.940, 1.503 | 1.141 | 0.742, 1.755 | ||
| Emotional/personal | 0.826 | 0.651, 1.047 | 0.558 | 0.334, 0.931 | ||
| Track | 3.060 | 1.176, 7.963 | 1.266 | 0.280, 5.731 | ||
| Days in program | 1.002 | 0.999, 1.004 | 1.007 | 1.002, 1.012 | ||
| Sessions | ||||||
| Individual | 1.333 | 1.201, 1.478 | 1.267 | 1.137, 1.412 | ||
| Group | 0.997 | 0.988, 1.006 | ||||
| Case management | 0.970 | 0.915, 1.028 | ||||
| Court | 0.987 | 0.965, 1.009 | ||||
| Sanctions | ||||||
| Community service | 0.995 | 0.806, 1.230 | ||||
| Jail | 0.613 | 0.477, 0.787 | 0.448 | 0.296, 0.678 | ||
| Behavior contract | 0.216 | 0.066, 0.702 | 0.212 | 0.021, 2.168 | ||
| Monetary fine | 0.396 | 0.182, 0.863 | 0.282 | 0.102, 0.781 | ||
Note: Variables with p < 0.20 in Models 1 & 2 were included in Model 3.
3.5.2 Multivariate model 2: program variables only
Multivariate model 2 included nine program variables with p-values under 0.20 in bivariate analyses (Table 5, column 2): days in program, individual sessions, group sessions, case management sessions, and court sessions, as well as community service, jail, behavior contract, and monetary fine sanctions. In the adjusted model, four of these variables significantly related to graduation (individual sessions, jail sanctions, behavior contract sanctions, and monetary fine sanctions) and, thus, progressed to the final multivariate model. Although days in program produced a p-value greater than 0.20, the final multivariate model included days in program as a control.
3.5.3 Multivariate model 3: final model
The final multivariate model included the following ten variables: age, criminogenic DOC, LSI-R alcohol/drugs subscale, LSI-R emotional/personal subscale, track, days in program, individual sessions, jail sanctions, behavior contract sanctions, and monetary fine sanctions. Only one participant risk variable significantly predicted graduation in the final model. Participants with higher scores on the LSI-R emotional/personal subscale (i.e., higher emotional/personal risk) were significantly less likely to graduate (OR: 0.56, CI: 0.33, 0.93).
In contrast, multiple program variables predicted graduation in the final model including days in program, individual counseling sessions, jail sanctions, and monetary fine sanctions. Adjusting for participant risk variables, attending more individual counseling sessions was significantly associated with increased odds of graduation (aOR: 1.267, CI: 1.137, 1.412). Receiving more jail sanctions (aOR: 0.448, CI: 0.296, 0.678) and monetary fine sanctions (aOR: 0.282, CI: 0.102, 0.781) was associated with lower odds of graduation, even after controlling for variation in participant risk. Age, criminogenic DOC, LSI-R alcohol/drug subscale, track, and behavior contract sanctions were not significant predictors of graduation in the final model.
4. Discussion
We hypothesized that both participant risk and program variables would significantly predict graduation. In bivariate analyses, multiple participant risk variables significantly related to graduation including age, DOC, and six LSI-R subscales: education/employment, accommodations, leisure/recreation, companions, alcohol/drugs, and emotional/personal. However, after controlling for program variables, only baseline emotional/personal risk remained a statistically significant predictor of graduation. In the final adjusted model, three program variables emerged as significant predictors of graduation: individual sessions, jail sanctions, and monetary fine sanctions.
In line with our hypotheses, age, DOC, and the LSI-R education/employment subscale significantly predicted drug court graduation in bivariate analyses. However, none of these variables significantly predicted graduation in the final adjusted model. The addition of programmatic variables in the adjusted model—a unique aspect of our study—may explain this divergence from past research. Contrary to our hypotheses and existing research, race, gender, and criminal history did not predict drug court graduation in bivariate analyses.
Only one participant risk factor significantly predicted graduation in the final adjusted model: emotional/personal risk at baseline. The LSI-R emotional/personal subscale included five items measuring 1) mild anxiety/depression and stressors; 2) active psychosis or severe emotional problems; 3) past mental health treatment; 4) present mental health treatment; and 5) anti-social personality traits (
Andrews and Bonta, 2000
). Importantly, prior studies on drug court effectiveness rarely reported mental health characteristics (Brown, 2010b
). Although, among the limited number of relevant existing studies, most found negative associations between emotional/personal problems and drug court graduation (Gray and Saum, 2005
; Mendoza et al., 2013
; Shannon et al., 2016
; Young and Belenko, 2002
). Our finding adds to this growing body of literature and suggests that existing drug court services should attend carefully to mental health issues. Future research should explore new methods and interventions to increase graduation rates among drug court participants with emotional/personal problems at baseline. For example, Smelson et al., 2020
recently pilot tested an intervention for individuals with co-occurring mental health and SUDs participating in drug court with promising results, although more rigorous research is needed to further test the intervention's effectiveness. Mental health issues often result from trauma, and drug court clients experience high levels of trauma (Giordano et al., 2016
; Sartor et al., 2012
). Addressing trauma and its impacts could help to increase drug court success (Gallagher and Nordberg, 2017
).After adjusting for participant risk variables and length of time in program, the number of individual counseling sessions significantly predicted drug court graduation, while group, case management, and court sessions did not. These findings align with those of a qualitative study of 31 drug court participants, wherein 80% of participants felt that individual counseling was the most beneficial component of drug court, while more than half expressed dissatisfaction with group sessions, often describing them as “chaotic” and “disorderly” (
Fulkerson et al., 2012
). Nevertheless, individual counseling session attendance merely may have indicated adherence to drug court requirements, and those who adhered to program expectations graduated at higher rates. Moreover, unmeasured aspects of drug court programming (e.g., time in recovery residence housing) could have confounded the observed associations between program variables and graduation. Thus, we do not purport a causal relationship between individual counseling and drug court graduation. More rigorous research should assess whether individual counseling causally impacts drug court graduation, and if drug courts should increase individual counseling provision as funding allows.- Fulkerson A.
- Keena L.D.
- O'Brien E.
Understanding success and nonsuccess in drug court.
The International Journal of Comparative Criminology. 2012; 57: 1297-1316https://doi.org/10.1177/0306624X12447774
Even after controlling for variation in participant risk factors, both jail and monetary fine sanctions were associated with lower likelihood of graduation. Other studies also found negative associations between jail sanctions and drug court graduation (
Goldkamp et al., 2001
; Hepburn and Harvey, 2007
; Rempel et al., 2016
; Wu et al., 2012
). This finding is unsurprising, as jail sanctions are the most extreme sanction and likely received on the path toward being terminated from drug court. Furthermore, to our knowledge, no studies have examined the association between monetary fine sanctions and drug court graduation; therefore, our finding of a negative association between receiving more monetary fine sanctions and graduation was unique. However, similar to jail sanctions, monetary fine sanctions may logically occur as a participant moves toward program termination.Considering the correlational nature of this study, readers should consider our findings of negative associations between jail and monetary fine sanctions and drug court graduation in light of existing evidence. A recent multisite randomized controlled trial indicated that “swift, certain, and fair” (SCF) sanctions significantly reduced substance use for individuals under criminal justice supervision (
Humphreys and Kilmer, 2020
) but produced no overall effects on recidivism (Lattimore et al., 2016
). Several less rigorous studies also suggested that SCF sanctions reduce substance use among justice-involved individuals with SUDs (see for example, Grommon et al., 2013
; Hawken and Kleiman, 2009
; Kunkel and White, 2013
; Shannon et al., 2015
). The effect of SCF sanctions on recidivism is less well-established, with some studies showing positive effects and others indicating null effects (Swift Certain Fair Resource Center, 2018, March 16
). Nonetheless, participants in our sample that received jail and monetary fine sanctions graduated at lower rates, which may indicate an appropriate timepoint for additional intervention (i.e., after receiving jail/monetary fine sanctions). Thus, drug court staff and researchers should explore ways to increase the likelihood of graduation among participants who receive jail and monetary fine sanctions.- Swift Certain Fair Resource Center
Annotated SCF literature review.
https://scfcenter.org/bja/annotated-scf-literature-review/
Date: 2018, March 16
Strengths of our study included analyzing both participant risk factors and program characteristics as predictors of drug court graduation. By using data from a drug court that serves both moderate- and high-risk clients, we provided a unique contribution to the current body of drug court research, which typically over-represents lower risk clients. Our study included several limitations. First, this study used programmatic data on hand from the drug court. The data did not include information about the type and quality of treatment sessions or time in recovery residence housing, which could be associated with retention and graduation rates. The available data also included little information on response to treatments and how that might impact leaving or staying in drug courts, as well as relatively little information on prior drug history and past treatments attempts. This limitation is important, as individuals with substance use problems typically participate in treatment multiple times before achieving success (
Kelly et al., 2019
). A second weakness relates to the lack of information about what triggered participants to drop out of drug court. Understanding not only who drops out of drug court, but why, could help researchers to identify additional necessary interventions (e.g., motivational, mental health). Third, the sample includes individuals from only one drug court, so our findings may not generalize to drug courts in other regions of the country, or with different participant characteristics.5. Conclusion
Approximately 30–50% of drug court participants do not complete drug court (
Dematteo et al., 2009
). This study examined the role of individual and programmatic factors on graduation from a felony-level, post-adjudication, adult drug court program, which targeted a population at significant risk for recidivism with multiple needs that increased their likelihood for recidivism and/or could interfere with treatment engagement. This study found that both participant characteristics and programmatic factors predicted drug court graduation. Particularly, individuals with high emotional/personal needs were less likely to graduate, and may benefit from tailored drug court services (e.g., trauma-informed care). Furthermore, individuals who attended more individual counseling sessions experienced a higher likelihood of graduation and those who received more jail and monetary fine sanctions experienced a lower likelihood of graduation. Experimental studies should assess the causal impact of individual counseling on drug court graduation to determine if wide-scale programmatic changes are justified. Last, drug court staff and researchers should explore methods to help modify behavior and increase the likelihood of graduation among participants who receive jail and monetary fine sanctions.CRediT authorship contribution statement
Olivia Randall-Kosich: Conceptualization, Methodology, Formal Analysis, Writing – Original Draft, Visualization. Daniel Whitaker: Conceptualization, Methodology, Data Curation, Writing – Review & Editing, Supervision. Wendy Guastaferro: Conceptualization, Writing – Review & Editing, Supervision. Danielle Rivers: Writing – Review & Editing.
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Article info
Publication history
Published online: October 28, 2021
Accepted:
October 20,
2021
Received in revised form:
September 7,
2021
Received:
March 18,
2021
Identification
Copyright
© 2021 Elsevier Inc. All rights reserved.
