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Predicting drug court graduation: Examining the role of individual and programmatic characteristics

Published:October 28, 2021DOI:https://doi.org/10.1016/j.jsat.2021.108654

      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 L.C.
      • Chenault K.I.
      • Dolan P.R.
      • Ganzi V.F.
      • Tuggle C.C.
      • Roth M.I.
      Behind bars II: Substance abuse and America’s prison population.
      ). Since the 1990s, drug courts helped to address this SUD treatment gap and reduce crime simultaneously (
      • Lurigio A.J.
      The first 20 years of drug treatment courts: A brief description of their history and impact.
      ). A large body of evidence suggests that drug courts reduce recidivism (
      • Mitchell O.
      • Wilson D.B.
      • Eggers A.
      • MacKenzie D.L.
      Assessing the effectiveness of drug courts on recidivism: A meta-analytic review of traditional and non-traditional drug courts.
      ). Some studies point to reductions in drug use, as well (
      • Gottfredson D.C.
      • Kearley B.W.
      • Najaka S.S.
      • Rocha C.M.
      The Baltimore City drug treatment court: 3-year self-report outcome study.
      ). 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 D.
      • Marlowe D.B.
      • Festinger D.S.
      • Arabia P.L.
      Outcome trajectories in drug court: Do all participants have drug Problems?.
      ). 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 D.
      • Turner S.
      • Wenzel S.
      • Morral A.
      • Harrell A.
      • McBride D.
      • Iguchi M.
      Drug courts: A conceptual framework.
      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 D.
      • Turner S.
      • Wenzel S.
      • Morral A.
      • Harrell A.
      • McBride D.
      • Iguchi M.
      Drug courts: A conceptual framework.
      ). One domain of structural characteristics is severity of need (i.e., population severity), which describes the characteristics of the drug court participants.
      • Longshore D.
      • Turner S.
      • Wenzel S.
      • Morral A.
      • Harrell A.
      • McBride D.
      • Iguchi M.
      Drug courts: A conceptual framework.
      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 A.O.
      • Boyle S.W.
      • Tollefson D.R.
      Factors that predict drug court completion and drop out: Findings from an evaluation of salt Lake County's adult felony drug court.
      ;
      • Mateyoke-Scrivner A.
      • Webster J.M.
      • Staton M.
      • Leukefeld C.
      Treatment retention predictors of drug court participants in a rural state.
      ;
      • Stageberg P.
      • Wilson B.
      • Moore R.G.
      Final report on the Polk County adult drug court.
      ;
      • Wolf E.M.
      • Sowards K.A.
      • Wolf D.A.
      Predicting retention of drug court participants using event history analysis.
      ;
      • DeVall K.E.
      • Lanier C.L.
      Successful completion: An examination of factors influencing drug court completion for white and non-white male participants.
      ), female (
      • Gallagher J.R.
      • Wahler E.A.
      • Nordberg A.
      Predictors of graduation and criminal recidivism: Findings from a drug court that primarily serves african americans.
      ;
      • Gray A.R.
      • Saum C.A.
      Mental health, gender, and drug court completion.
      ;
      • Hartman J.L.
      • Listwan S.J.
      • Shaffer D.K.
      Methamphetamine users in a community-based drug court: Does gender matter?.
      ;
      • Stageberg P.
      • Wilson B.
      • Moore R.G.
      Final report on the Polk County adult drug court.
      ), White vs. non-White (
      • Brewster M.P.
      An evaluation of the Chester County (PA) drug court program.
      ;
      • Butzin C.A.
      • Saum C.A.
      • Scarpitti F.R.
      Factors associated with completion of a drug treatment court diversion program.
      ;
      • Fulkerson A.
      • Keena L.D.
      • O'Brien E.
      Understanding success and nonsuccess in drug court.
      ;
      • Gray A.R.
      • Saum C.A.
      Mental health, gender, and drug court completion.
      ;
      • Hartley R.E.
      • Phillips R.C.
      Who graduates from drug courts? Correlates of client success.
      ;
      • Ho T.
      • Carey S.M.
      • Malsch A.M.
      Racial and gender disparities in treatment courts: Do they exist and is there anything we can do to change them?.
      ;
      • Mateyoke-Scrivner A.
      • Webster J.M.
      • Staton M.
      • Leukefeld C.
      Treatment retention predictors of drug court participants in a rural state.
      ;
      • Shah S.
      • DeMatteo D.
      • Keesler M.
      • Davis J.
      • Heilbrun K.
      • Festinger D.S.
      Addiction severity scores and urine drug screens at baseline as predictors of graduation from drug court.
      ;
      • Shannon L.M.
      • Jackson Jones A.
      • Perkins E.
      • Newell J.
      • Neal C.
      Examining individual factors and during-program performance to understand drug court completion.
      ;
      • Stageberg P.
      • Wilson B.
      • Moore R.G.
      Final report on the Polk County adult drug court.
      ), employed (
      • Butzin C.A.
      • Saum C.A.
      • Scarpitti F.R.
      Factors associated with completion of a drug treatment court diversion program.
      ;
      • Gallagher J.R.
      • Wahler E.A.
      • Nordberg A.
      Predictors of graduation and criminal recidivism: Findings from a drug court that primarily serves african americans.
      ;
      • Hartley R.E.
      • Phillips R.C.
      Who graduates from drug courts? Correlates of client success.
      ;
      • Listwan S.J.
      • Shaffer D.K.
      • Hartman J.L.
      Combating methamphetamine use in the community: The efficacy of the drug court model.
      ;
      • Mateyoke-Scrivner A.
      • Webster J.M.
      • Staton M.
      • Leukefeld C.
      Treatment retention predictors of drug court participants in a rural state.
      ;
      • Roll J.M.
      • Prendergast M.
      • Richardson K.
      • Burdon W.
      • Ramirez A.
      Identifying predictors of treatment outcome in a drug court program.
      ;
      • Stageberg P.
      • Wilson B.
      • Moore R.G.
      Final report on the Polk County adult drug court.
      ;
      • Wu L.J.
      • Altshuler S.J.
      • Short R.A.
      • Roll J.M.
      Predicting drug court outcome among amphetamine-using participants.
      ), and more educated (
      • Gill M.E.
      Predictors of drug court client graduation.
      ;
      • Gray A.R.
      • Saum C.A.
      Mental health, gender, and drug court completion.
      ;
      • Listwan S.J.
      • Shaffer D.K.
      • Hartman J.L.
      Combating methamphetamine use in the community: The efficacy of the drug court model.
      ;
      • Mateyoke-Scrivner A.
      • Webster J.M.
      • Staton M.
      • Leukefeld C.
      Treatment retention predictors of drug court participants in a rural state.
      ;
      • Shah S.
      • DeMatteo D.
      • Keesler M.
      • Davis J.
      • Heilbrun K.
      • Festinger D.S.
      Addiction severity scores and urine drug screens at baseline as predictors of graduation from drug court.
      ;
      • Shannon L.M.
      • Jackson Jones A.
      • Perkins E.
      • Newell J.
      • Neal C.
      Examining individual factors and during-program performance to understand drug court completion.
      ;
      • Stageberg P.
      • Wilson B.
      • Moore R.G.
      Final report on the Polk County adult drug court.
      ). Multiple studies also suggest that individuals with more serious criminal histories (
      • Gallagher J.R.
      • Wahler E.A.
      • Nordberg A.
      Predictors of graduation and criminal recidivism: Findings from a drug court that primarily serves african americans.
      ;
      • Gray A.R.
      • Saum C.A.
      Mental health, gender, and drug court completion.
      ;
      • Mateyoke-Scrivner A.
      • Webster J.M.
      • Staton M.
      • Leukefeld C.
      Treatment retention predictors of drug court participants in a rural state.
      ;
      • Miller J.M.
      • Shutt J.E.
      Considering the need for empirically grounded drug court screening mechanisms.
      ;
      • Peters R.H.
      • Haas A.L.
      • Murrin M.R.
      Predictors of retention and arrest in drug courts.
      ;
      • Rempel M.
      • DeStefano C.D.
      Predictors of engagement in court-mandated treatment: Findings at the Brooklyn treatment court, 1996–2000.
      ;
      • Shannon L.M.
      • Jackson Jones A.
      • Perkins E.
      • Newell J.
      • Neal C.
      Examining individual factors and during-program performance to understand drug court completion.
      ;
      • Wolf E.M.
      • Sowards K.A.
      • Wolf D.A.
      Predicting retention of drug court participants using event history analysis.
      ) and those who use harder drugs (e.g., cocaine, opioids) are less likely to graduate (
      • Brown R.
      Associations with substance abuse treatment completion among drug court participants.
      ;
      • Hartley R.E.
      • Phillips R.C.
      Who graduates from drug courts? Correlates of client success.
      ;
      • Hickert A.O.
      • Boyle S.W.
      • Tollefson D.R.
      Factors that predict drug court completion and drop out: Findings from an evaluation of salt Lake County's adult felony drug court.
      ;
      • Mateyoke-Scrivner A.
      • Webster J.M.
      • Staton M.
      • Leukefeld C.
      Treatment retention predictors of drug court participants in a rural state.
      ;
      • Wolf E.M.
      • Sowards K.A.
      • Wolf D.A.
      Predicting retention of drug court participants using event history analysis.
      ). A few studies also found that co-occurring mental health problems (
      • Mendoza N.S.
      • Trinidad J.R.
      • Nochajski T.H.
      • Farrell M.C.
      Symptoms of depression and successful drug court completion.
      ;
      • Shannon L.M.
      • Jackson Jones A.
      • Perkins E.
      • Newell J.
      • Neal C.
      Examining individual factors and during-program performance to understand drug court completion.
      ) and SUD severity (
      • Butzin C.A.
      • Saum C.A.
      • Scarpitti F.R.
      Factors associated with completion of a drug treatment court diversion program.
      ;
      • Gray A.R.
      • Saum C.A.
      Mental health, gender, and drug court completion.
      ;
      • Shah S.
      • DeMatteo D.
      • Keesler M.
      • Davis J.
      • Heilbrun K.
      • Festinger D.S.
      Addiction severity scores and urine drug screens at baseline as predictors of graduation from drug court.
      ) 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 J.S.
      • White M.D.
      • Robinson J.B.
      Do drug courts work? Getting inside the drug court black box.
      ;
      • Shannon L.M.
      • Jackson Jones A.
      • Perkins E.
      • Newell J.
      • Neal C.
      Examining individual factors and during-program performance to understand drug court completion.
      ). 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 D.K.
      Looking inside the black box of drug courts: A meta-analytic review.
      ). 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 (;
      • Belenko S.
      • Fabrikant N.
      • Wolff N.
      The long road to treatment: Models of screening and admission into drug courts.
      ). 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 J.R.
      • Wahler E.A.
      • Nordberg A.
      Predictors of graduation and criminal recidivism: Findings from a drug court that primarily serves african americans.
      ). 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 D.
      • Turner S.
      • Wenzel S.
      • Morral A.
      • Harrell A.
      • McBride D.
      • Iguchi M.
      Drug courts: A conceptual framework.
      ).

      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 W.P.
      • Daigle L.E.
      Linking noncompliant behaviors and programmatic responses: The use of graduated sanctions in a felony-level drug court.
      ). 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 D.A.
      • Bonta J.
      The level of service inventory-revised.
      ) and 2) the Texas Christian University Drug Screen or TCUDS (
      • Institute of Behavioral Research
      Texas Christian University drug screen 5.
      ). 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 W.P.
      • Daigle L.E.
      Linking noncompliant behaviors and programmatic responses: The use of graduated sanctions in a felony-level drug court.
      ). 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 D.A.
      • Bonta J.
      The psychology of criminal conduct.
      ;
      • Taxman F.
      • Pattavina A.
      • Caudy M.
      • Byrne J.
      • Durso J.
      The Empirical basis for the RNR model with an updated RNR conceptual framework.
      ). Primary criminogenic needs include substance use for individuals with SUDs involving substances with an empirically based and direct relationship with criminal behavior (
      • Bennett T.
      • Holloway K.
      • Farrington D.
      The statistical association between drug misuse and crime: A meta-analysis.
      ;
      • Pierce M.
      • Hayhurst K.
      • Bird S.M.
      • Hickman M.
      • Seddon T.
      • Dunn G.
      • Millar T.
      Quantifying crime associated with drug use among a large cohort of sanctioned offenders in England and Wales.
      ;
      • Taxman F.
      • Pattavina A.
      • Caudy M.
      • Byrne J.
      • Durso J.
      The Empirical basis for the RNR model with an updated RNR conceptual framework.
      ). Marijuana and alcohol use may merit intervention but do not have a direct relationship with criminal behavior (with the exception of DUI; see
      • Taxman F.
      • Pattavina A.
      • Caudy M.
      • Byrne J.
      • Durso J.
      The Empirical basis for the RNR model with an updated RNR conceptual framework.
      ). 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 F.
      • Pattavina A.
      • Caudy M.
      • Byrne J.
      • Durso J.
      The Empirical basis for the RNR model with an updated RNR conceptual framework.
      ).
      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 D.A.
      • Bonta J.
      The level of service inventory-revised.
      ).
      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
       % male77.773.183.20.059
       % Black61.869.952.20.004
       Age, M (SD)38.9 (11.2)41.6 (10.8)35.8 (10.8)<0.001
       % married10.89.112.80.350
       % graduated high school53.840.469.8<0.001
       % employed38.419.860.9<0.001
      Substance use characteristics
       % cocaine drug of choice55.465.742.40.001
       % criminogenic drug of choice81.988.074.10.013
       % polydrug use50.957.043.90.056
      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 history4.6 (2.0)5.2 (1.6)3.9 (2.2)<0.001
      Education/employment4.2 (2.7)5.6 (2.4)2.6 (2.1)<0.001
      Financial1.1 (0.7)1.3 (0.6)0.8 (0.7)<0.001
      Family/marital1.9 (1.3)2.4 (1.3)1.4 (1.2)<0.001
      Accommodations1.0 (1.1)1.5 (1.1)0.4 (0.7)<0.001
      Leisure/recreation1.5 (0.8)1.6 (0.7)1.3 (0.9)<0.001
      Companions2.5 (1.4)2.9 (1.4)2.0 (1.2)<0.001
      Alcohol/drugs6.5 (2.1)7.0 (1.8)5.9 (2.3)<0.001
      Emotional/personal1.6 (1.4)1.6 (1.5)1.6 (1.4)0.778
      Attitudes/orientations0.4 (0.8)0.6 (0.9)0.1 (0.3)<0.001
      Program variables
      % graduated drug court45.838.154.90.008
      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 sanction83.485.181.40.441
       % received treatment sanction5.35.25.30.976
       % received community service sanction56.360.551.30.150
       % received jail sanction74.576.971.70.352
       % received behavior contract sanction9.77.512.40.193
       % received 30-day step up sanction32.820.946.9<0.001
       % received monetary fine sanction18.217.918.60.891
      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
      % gradOR95% CIp-Value
      Demographic characteristics
      Sex
       Female233242Ref
       Male90102471.2280.670, 2.2510.5073
      Race
       Black678544Ref
       Non-Black4846491.2160.726, 2.0360.4578
      Age (mean, SD)42.3, 9.935.4, 11.41.0621.033, 1.091<0.0001
      Marital status
       Not married9711845Ref
       Married1313501.2160.539, 2.7460.6372
      Education at enrollment
       Did not graduate HS435444Ref
       Graduated HS6350561.5820.916, 2.7320.0996
      Employment at enrollment
       Unemployed576846Ref
       Employed4632591.7150.968, 3.0390.0647
      Substance use characteristics
      Drug of choice (DOC)
       DOC is “criminogenic”8177512.2951.053, 5.0000.0366
      Cocaine/crack cocaine575053
      Heroin/other opiates121643
      Methamphetamines/other amphetamines121152
       DOC is not “criminogenic”112431Ref
      Alcohol21017
      Marijuana81044
      Other1420
      Polydrug use
       Polydrug use545450Ref
       Single drug use5450521.0800.630, 1.8510.7795
      Track
       Track 1 (higher risk)518338Ref
       Track 2 (lower risk)6251551.9781.189, 3.2910.0086
      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.
      Arrests before enrollment.
      Graduates

      (N = 113)
      Non-graduates

      (N = 134)
      OR95% CIp-Value
      MSDMSD
      Arrest historya
      Total arrests16.6113.4017.7614.910.9940.976, 1.0130.5393
      Felony arrests9.708.4410.1710.180.9950.967, 1.0230.7004
      Violent arrests0.721.130.771.260.9620.774, 1.1960.7277
      Drug-related arrests2.432.262.252.361.0340.924, 1.1570.5603
      Risk-needs assessment
      LSI-R total score (54 items)23.069.2726.747.140.9480.916, 0.9800.0020
      Criminal history (10)4.492.034.702.000.9490.829, 1.0850.4423
      Education/employment (10)3.792.684.592.650.8930.806, 0.9900.0313
      Financial (2)0.960.681.140.660.6680.444, 1.0040.0520
      Family/marital (4)1.841.351.981.340.9260.757, 1.1320.4540
      Accommodations (3)0.831.031.151.100.7540.583, 0.9770.0324
      Leisure/recreation (2)1.290.871.660.620.5140.352, 0.7500.0006
      Companions (5)2.251.392.781.330.7520.613, 0.9220.0062
      Alcohol/drugs (9)6.142.456.911.580.8330.727, 0.9550.0089
      Emotional/personal (5)1.321.341.931.430.7300.598, 0.8920.0020
      Attitudes/orientations (4)0.370.840.360.651.0260.716, 1.4710.8873
      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)
      OR95% CIp-Value
      MSDMSD
      Days in program785.00152.06494.18379.241.0041.003, 1.005<0.0001
      Treatment sessionsaOR
      aOR controlling for days in program.
       Total sessions236.1280.67173.82117.221.0020.998, 1.0060.3175
       Individual counseling27.155.6913.4511.751.1951.121, 1.274<0.0001
       Group counseling189.6178.18118.36102.851.0041.000, 1.0080.0429
       Case management19.3610.0412.5313.781.0240.992, 1.0560.1402
       Court68.5021.8663.3841.390.9900.980, 1.0010.0715
      SanctionsaOR
      aOR controlling for days in program.
       Total sanctions5.325.155.636.220.8510.795, 0.911<0.0001
       Treatment response0.090.370.040.210.9660.339, 2.7510.9487
       Community service2.012.551.652.470.8380.731, 0.9610.0112
       Jail2.262.332.872.800.6800.586, 0.790<0.0001
       Behavior contract0.050.230.170.510.0890.031, 0.256<0.0001
       30-day step up0.731.160.571.220.9190.708, 1.1920.5233
       Monetary fine0.190.490.330.750.3390.190, 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.
      VariableModel 1: Participant onlyModel 2: Program onlyModel 3: Final model
      aOR95% CIaOR95% CIaOR95% CI
      Age1.0791.039, 1.1211.0240.960, 1.093
      Drug of choice (DOC)
       Criminogenic DOC1.8320.768, 4.3660.7790.130, 4.687
       Non-criminogenic DOCRefRef
      LSI-R sub-scales
       Education/employment1.0460.894, 1.223
       Financial0.7320.416, 1.286
       Accommodations0.9000.640, 1.267
       Leisure/recreation0.7410.440, 1.247
       Companions0.9700.728, 1.292
       Alcohol/drugs1.1880.940, 1.5031.1410.742, 1.755
       Emotional/personal0.8260.651, 1.0470.5580.334, 0.931
      Track3.0601.176, 7.9631.2660.280, 5.731
      Days in program1.0020.999, 1.0041.0071.002, 1.012
      Sessions
       Individual1.3331.201, 1.4781.2671.137, 1.412
       Group0.9970.988, 1.006
       Case management0.9700.915, 1.028
       Court0.9870.965, 1.009
      Sanctions
       Community service0.9950.806, 1.230
       Jail0.6130.477, 0.7870.4480.296, 0.678
       Behavior contract0.2160.066, 0.7020.2120.021, 2.168
       Monetary fine0.3960.182, 0.8630.2820.102, 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 D.A.
      • Bonta J.
      The level of service inventory-revised.
      ). Importantly, prior studies on drug court effectiveness rarely reported mental health characteristics (
      • Brown R.T.
      Systematic review of the impact of adult drug-treatment courts.
      ). Although, among the limited number of relevant existing studies, most found negative associations between emotional/personal problems and drug court graduation (
      • Gray A.R.
      • Saum C.A.
      Mental health, gender, and drug court completion.
      ;
      • Mendoza N.S.
      • Trinidad J.R.
      • Nochajski T.H.
      • Farrell M.C.
      Symptoms of depression and successful drug court completion.
      ;
      • Shannon L.M.
      • Jackson Jones A.
      • Perkins E.
      • Newell J.
      • Neal C.
      Examining individual factors and during-program performance to understand drug court completion.
      ;
      • Young D.
      • Belenko S.
      Program retention and perceived coercion in three models of mandatory drug treatment.
      ). 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 D.
      • Shaffer P.M.
      • Rodriguez C.P.
      • Gaba A.
      • Harter J.
      • Pinals D.A.
      • Casey S.C.
      A co-occurring disorders intervention for drug treatment court: 12-month pilot study outcomes.
      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 A.L.
      • Prosek E.A.
      • Stamman J.
      • Callahan M.M.
      • Loseu S.
      • Bevly C.M.
      • Chadwell K.
      Addressing trauma in substance abuse treatment.
      ;
      • Sartor C.E.
      • McCutcheon V.V.
      • O'Leary C.C.
      • Van Buren D.J.
      • Allsworth J.E.
      • Jeffe D.B.
      • Cottler L.B.
      Lifetime trauma exposure and posttraumatic stress disorder in women sentenced to drug court.
      ). Addressing trauma and its impacts could help to increase drug court success (
      • Gallagher J.R.
      • Nordberg A.
      A phenomenological and grounded theory study of women’s experiences in drug court: Informing practice through a gendered lens.
      ).
      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 A.
      • Keena L.D.
      • O'Brien E.
      Understanding success and nonsuccess in drug court.
      ). 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.
      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 J.S.
      • White M.D.
      • Robinson J.B.
      Do drug courts work? Getting inside the drug court black box.
      ;
      • Hepburn J.R.
      • Harvey A.N.
      The effect of the threat of legal sanction on program retention and completion: Is that why they stay in drug court?.
      ;
      • Rempel M.
      • Fox-Kralstein D.
      • Cissner A.
      • Cohen R.
      • Labriola M.
      • Farole D.
      • Magnani M.
      The New York state adult drug court evaluation: Policies, participants and impacts.
      ;
      • Wu L.J.
      • Altshuler S.J.
      • Short R.A.
      • Roll J.M.
      Predicting drug court outcome among amphetamine-using participants.
      ). 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 K.
      • Kilmer B.
      Still HOPEful: Reconsidering a “failed” replication of a swift, certain, and fair approach to reducing substance use among individuals under criminal justice supervision.
      ) but produced no overall effects on recidivism (
      • Lattimore P.K.
      • MacKenzie D.L.
      • Zajac G.
      • Dawes D.
      • Arsenault E.
      • Tueller S.
      Outcome findings from the HOPE demonstration field experiment: Is swift, certain, and fair an effective supervision strategy?.
      ). Several less rigorous studies also suggested that SCF sanctions reduce substance use among justice-involved individuals with SUDs (see for example,
      • Grommon E.
      • Cox S.M.
      • Davidson W.S.
      • Bynum T.S.
      Alternative models of instant drug testing: Evidence from an experimental trial.
      ;
      • Hawken A.
      • Kleiman M.
      Managing drug involved probationers with swift and certain sanctions: Evaluating Hawaii’s HOPE: Executive summary.
      ;
      • Kunkel T.L.
      • White M.T.
      Arkansas SWIFT courts: Implementation assessment and long-term evaluation plan.
      ;
      • Shannon L.M.
      • Hulbig S.K.
      • Birdwhistell S.
      • Newell J.
      • Neal C.
      Implementation of an enhanced probation program: Evaluating process and preliminary outcomes.
      ). 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
      Annotated SCF literature review.
      ). 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.
      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 J.F.
      • Greene M.C.
      • Bergman B.G.
      • White W.L.
      • Hoeppner B.B.
      How many recovery attempts does it take to successfully resolve an alcohol or drug problem? Estimates and correlates from a national study of recovering US adults.
      ). 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 D.
      • Marlowe D.B.
      • Festinger D.S.
      • Arabia P.L.
      Outcome trajectories in drug court: Do all participants have drug Problems?.
      ). 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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