Treating High-Risk Offenders in the Community

Abstract

The drug court model, which integrates drug treatment with community supervision and uses the authority of the court to facilitate compliance and behavioral change, provides an innovative alternative to processing as usual. While drug courts have enjoyed considerable empirical support, research suggests that they could increase their effectiveness through further refining their target population. In particular, it is hypothesized that drug courts are particularly well suited to treat drug offenders who have a high risk for recidivism. The purpose of the current study is to compare recidivism rates of high-risk drug court participants and high-risk probationers. Using new charges as a measure of recidivism, the results indicate drug court participants had significantly better outcomes than probationers. Implications for policy and practice are discussed.

Keywords

drug court recidivism LSI-R high risk substance abuse community-based treatment

Introduction

An administrative order from Florida’s 11th judicial circuit established the nation’s first drug court in Miami in 1989. Then-Associate Chief Judge Herbert Klein, who led the design and implementation of the drug court, explained its underlying rationale, “Putting more and more offenders on probation just perpetuates the problem. The same people are picked up again and again until they end up in the state penitentiary and take up space that should be used for violent offenders” (Drug Strategies, 1997, p. 6). The drug court model has grown exponentially since then, with over 2,600 drug courts providing services to more than 120,000 people by the end of 2010 (National Association of Drug Court Professionals [NADCP], 2012).

Judge Klein’s account provides context for understanding the growth of the drug court model. The courts have been inundated with offenders who were using drugs or alcohol at the time of the offense with estimates suggesting that over half of all sentenced offenders met the criteria for drug abuse or dependence (Chandler, Fletcher, & Volkow, 2009). The judiciary traditionally had two options for sentencing offenders: prison or probation. Yet, neither is particularly effective for individuals with substance abuse problems. Offenders with drug charges, or with a history of drug abuse, are more likely to reoffend upon release from prison than those sentenced to probation while probationers often struggle to comply with the terms of their supervision and stay clean (Spohn & Holleran, 2002). The difficulties drug offenders experience is highlighted in Mumola and Karberg’s (2006) finding that the majority of arrestees with a substance abuse problem report at least three prior sentences to probation or incarceration.

The drug court model, which integrates drug treatment, community supervision, and the authority of the court to facilitate compliance and behavioral change, provides an innovative alternative to business as usual. Drug courts have enjoyed considerable empirical support. For example, research finds that drug courts retain clients in treatment longer than community-based treatment programs (Belenko, 2002a; Taxman & Bouffard, 2003), reduce in-program recidivism (Peters & Murrin, 2000), increase employment and/or educational attainment (Rempel et al., 2003), and reduce post-program drug use and criminal behavior (Government Accountability Office [GAO], 2011; Mitchell, Wilson, Eggers, & MacKenzie, 2012; Shaffer, 2006, 2011; Wilson, Mitchell, & Mackenzie, 2006).

Despite the success of drug courts, there are continued calls for improving the model. For example, it has been suggested that drug courts should more clearly articulate their target population, use actuarial assessments to measure risk and need, and match services to individual needs (Listwan, Shaffer, & Latessa, 2002; Marlowe, Festinger, Lee, Dugosh, & Benasutti, 2006). The issue of who drug courts should target is increasingly important in light of recent policy shifts in the community. In particular, many states are now moving to reduce their prison populations, and in some circumstances, mandate community-based treatment for drug offenders. The current study seeks to add to this literature by examining the impact of drug court participation among a sample of high-risk participants. Specifically, outcomes for high-risk drug offenders served within a drug court are compared with a matched sample of offenders receiving traditional probation supervision.

Literature Review

The incarceration of drug offenders during the War on Drugs created an organizational crisis in corrections. Current statistics estimate that 1 in every 100 adults is behind bars, with this statistic climbing to 1 in every 9 African American men ages 20 to 34 (Warren, 2008). Despite the wildly punitive shift that has occurred in American corrections, change appears on the horizon (Listwan, Jonson, Cullen, & Latessa, 2008). Grappling with the need to balance their budgets, states are enacting legislation to reduce the flow of drug offenders into the prison system. For example, a number of states are decreasing the length of sentences for drug and/or low-level nonviolent offenders (Greene, 2003) and other states have repealed laws (e.g., Rockefeller drug laws in New York; mandatory life sentences in Michigan) or changed the thresholds for drug trafficking (Greene & Schiraldi, 2002; New York State Department of Correctional Services, 2004).

Despite these shifts in policy, the number of offenders served by the criminal justice system remains at an all time high. For example, there were over 5 million people on probation and parole in 2009, up from 1.6 million 25 years earlier (Pew Center on the States, 2009). In addition, there has been a 274% increase in the number of people incarcerated over the past 25 years (Pew Center on the States, 2009). More troubling is that in many states, probation and parole revocations are a primary driver of prison populations. Revocations account for one quarter to one half of all new prison admissions with more individuals returning to prison for technical violations than new offenses (Hughes, Wilson, & Beck, 2001). The high rates of revocation suggest that community supervision and treatment programs are in need of improvement.

States are increasingly acknowledging the importance of treatment as evidenced by policies mandating substance abuse treatment for non-violent drug offenders. California, for example, formalized this process with the passage of Proposition 36, which requires the state to offer all eligible offenders up to 1 year of community-based drug treatment and 6 months of aftercare (California Department of Alcohol and Drug Programs, 2006; Longshore, Hawken, Urada, & Anglin, 2006). If the offender successfully completes the treatment, the charges are dismissed and the record is expunged (Rusche, 2000; Wittman, 2001). However, there are concerns that this policy will be difficult to implement. An examination of probation’s capacity to handle cases resulting from Proposition 36 found high rates of noncompliance within the existing community supervision structure. For example, more than 30% of those ordered to drug testing as a condition of probation absconded. The study also found that there was insufficient follow-through for behavior violations and illicit drug use was often not sanctioned (Kleiman et al., 2003).

The challenge, particularly where a policy like Proposition 36 is concerned, is that probation departments often lack sufficient resources and authority to adequately implement these types of policy mandates (Kleiman et al., 2003). Probation and parole have been particularly affected by the recent “do more with less” economic climate given the research that finds 7 of every 10 offenders are supervised in the community (vs. jail or prison), yet 9 of every 10 dollars are spent in corrections is funneled to support prisons (Pew Center on the States, 2009). In searching for alternatives to prison, drug courts are seen (by many) as a reasonable and cost-efficient option for offenders with drug problems.

Drug courts are generally viewed as successful given their impact on recidivism among drug offenders on community supervision. Meta-analyses consistently find that drug courts are effective, with average reductions in recidivism hovering around 10% (Aos, Phipps, Barnoski, & Lieb, 2001; Lowenkamp, Holsinger, & Latessa, 2005; Shaffer, 2011; Wilson et al., 2006). While lauded by the NADCP, the reality is that drug courts are likely underachieving relative to their potential. For instance, prior research has shown that the most effective correctional programs can reduce recidivism by 26% to 30% (Andrews et al., 1990; Dowden & Andrews, 1999, 2000, 2004). While there are some drug courts that likely see substantial reductions in recidivism, others fail to achieve these results. One explanation may be that drug courts often fail to target the most appropriate offenders for their services.

The risk principle holds that matching high-risk/needs offenders with intensive programming is the most effective and efficient approach to reducing recidivism and changing offender behavior (Andrews & Bonta, 2010). Adhering to this principle is associated with reductions in recidivism, while programs that violate this principle are often found to increase recidivism (Lowenkamp & Latessa, 2004). Given their intensive nature, drug courts are well suited to treat high-risk drug offenders. However, there is evidence to suggest they often target less serious drug offenders. For example, Mitchell et al. (2012) found that 20% of the samples included in their study had minor criminal histories. Similarly, Shaffer (2006) found that among drug courts reporting on criminal history, nearly half reported samples in which less than 50% had any type of prior record.

The desire to focus on low risk is expected for several reasons. Drug courts may often assess client’s severity of drug use without considering their overall risk to offend. Moreover, higher risk offenders are, by definition, more likely to recidivate and drug courts may exclude high-risk clients for fear of high recidivism rates. Finally, political pressure to be successful along with the desire to avoid negative publicity may also affect the selection of participants. Yet targeting lower risk offenders may have a number of unintended consequences including both net-widening and undermining the model’s effectiveness.

Treating and supervising offenders in the community is often associated with better outcomes and is more cost-effective than prison-based treatment. Though community-based treatment is generally advantageous to institutional treatment (Andrews, 1995; Spohn & Holleran, 2002), it is important to acknowledge the wide range of programs within the community setting. Some programs are better suited to high-risk offenders than others given high-risk offenders are in need of both intensive supervision and treatment. Drug courts can potentially fill this role for several reasons.

First, unlike other community-based programs, the drug court model involves direct and collaborative relationships between the treatment and criminal justice systems. Using a non-adversarial system, the court, probation, and treatment work together to screen clients, monitor progress, and provide feedback to participants. The interdisciplinary drug court team can address challenging clients or programmatic difficulties. This type of collaborative approach has benefits over other approaches. For example, when used as a stand-alone approach, referrals to treatment have largely proven ineffective at engaging individuals (Kleiman et al., 2003).

Second, research suggests that the consideration and use of both criminal justice and treatment information is thought to be an essential component of effective programming (Austin, 2004; Gottfredson & Moriarty, 2006; Lowenkamp, Latessa, & Smith, 2006; Vieira, Skilling, & Peterson-Baldali, 2009). The greater oversight between referral and service provision, implicit in the drug court model, is thought to increase the effectiveness of referrals (Wolff & Pogorzelski, 2005). In addition, the use of the court’s authority to ensure compliance and follow-up combined with supportive treatment and services set drug courts apart from other community-based programs by providing a framework for collaboration.

Third, the drug court model is also associated with better treatment retention rates (Belenko, 2002b). Research suggests that the ability to engage and retain clients in treatment increases an individual’s desire for change, which in turn improves outcomes (Brocato & Wagner, 2008; Hubbard, Craddock, & Anderson, 2003). In particular, research suggests that the length of stay in treatment may have the greatest impact on changing behavior (Lurigio, 2000; Simpson, Joe, Broome, et al., 1997; Simpson, Joe, & Brown, 1997). This finding is particularly salient for high-risk, high-need offenders who are more likely to drop out of treatment (Marlowe, DeMatteo, & Festinger, 2003).

Though research has examined retention and recidivism rates among drug court participants, there is relatively limited research exploring the role of risk in regard to drug court outcomes. In their meta-analysis, Lowenkamp et al. (2006) found that drug courts serving younger and higher risk participants were more effective than drug courts targeting older or lower risk participants. Specifically, they found reductions in recidivism up to 25% among those programs. Others have examined the importance of matching level of service to level of risk within the drug court setting. Using the Diagnostic and Statistical Manual of Mental Disorders (4th ed., DSM-IV; American Psychiatric Association, 1994) criteria for antisocial personality disorder (APD) as a proxy for risk, Festinger et al. (2002) examined the impact of the frequency of judicial status hearings on outcomes. They concluded that more frequent judicial status hearings were associated with longer periods of abstinence among high-risk offenders while fewer hearings were associated with better outcomes for lower risk offenders. Similarly, using APD or a history of drug treatment as a measure of risk, Marlowe and his colleagues (2006) found that higher risk participants who received more intensive services were less likely to have positive drug tests and were more likely to graduate when compared with higher risk participants who received standard services. Collectively, these findings provide preliminary support for serving high-risk drug offenders in community-based drug courts.

The current study examines recidivism rates of high-risk drug court participants and high-risk probationers. The study builds on the existing research in two ways. First, risk level was identified by a validated risk-needs assessment rather than relying on a proxy measure. Second, the current study explores longer term outcomes between two high-risk community-based groups. By comparing these two groups on recidivism, this study is able to isolate the impact of drug court participation on outcomes for this important population.

Method

Program Setting

The Ada County drug court, located in Boise, Idaho, is a post-adjudication, comprehensive outpatient, court-supervised program (Listwan, Borowiak, & Latessa, 2008; Listwan & Latessa, 2003; Shaffer, Hartman, & Listwan, 2009). The program began accepting clients in January 1999. To be eligible for this program, offenders must have been charged with a felony offense, but cannot have more than one prior conviction for a felony possession charge. Offenders who have been convicted of sex or dealing offenses, a history of violent crime, not accepting of guilt, identified as being too low risk, or residing outside of the county are typically deemed ineligible for the program.

The program, designed to last a minimum of 1 year, consists of four phases. Within each phase, offenders are subjected to regular urinalysis, participate in individual and group sessions, attend substance abuse education, and engage in other rehabilitative activities. Each phase lasts approximately 3 months. Phase 1 consists of at least two weekly urinalyses and participation in cognitive self-change, substance abuse education, and process groups. In Phase 2, offenders are drug tested at least once a week, participate in individual counseling sessions, and complete cognitive and substance abuse relapse packets. Phase 3 consists of at least one weekly urinalysis and individualized treatment concentrated on living and recovery. The final phase (Phase 4) requires the completion of the treatment plan, which is focused on using program tools geared toward long-term recovery and at least one weekly urinalysis.

Graduation requirements from the drug court include the completion of all treatment requirements and 6 months of clean drug tests. Participants lacking a high school education are required to obtain a GED or demonstrate they are taking the steps toward obtaining it (by taking classes and exams). Finally, participants must have full-time employment or be enrolled in school full-time, and have fully paid any restitution owed. Previous research has indicated the Ada County Drug Court has a 42% graduation rate (Listwan & Latessa, 2003).

Drug courts in Idaho are required to consider Level of Service Inventory (LSI-R)¹ results when selecting participants. Courts are encouraged to enroll clients who score between 25 and 41, although they do have discretion for accepting those who fall outside of this range.² A prior outcome evaluation of 14 felony drug courts from across the state found a significant increase in recidivism among participants scoring 34 and above (high risk) when compared with those scoring below 34 (moderate risk; Listwan, Borowiak, et al., 2008). The current study restricted the sample to offenders scoring 34 and above on the LSI-R. Our analysis focused on the largest felony drug court in the state.

Design and Sample

A quasi-experimental design was used to assess the impact of drug court participation. Experimental designs are ideal for social science research because they allow for adequate control of factors that can affect the internal validity in studies (Babbie, 2013). Using experimental designs helps control for rival causal factors that may influence the effect of treatment by utilizing randomization techniques to distribute the sample between treatment and control groups. However, while randomization is preferred, it is rarely feasible in evaluation research and quasi-experimental designs are often utilized instead. The current study used a non-equivalent matched comparison group to help ensure the groups are similar in an effort to avoid the influence of rival causal factors on the effect of treatment (Campbell & Stanley, 1963).

The treatment group consisted of clients who entered the Ada County (Boise) drug court between July 2002 and July 2005. The comparison group comprised of a matched sample of probationers in the same county. The comparison group consisted of men and women who were eligible for the drug court, but did not receive its services. The comparison group was selected through the Idaho Department of Corrections database on drug offenders who were being served on probation. Individuals were selected by filtering those adults who were on probation in Ada County yet not served by the drug court.³ The comparison group was then further matched to the drug court group on the basis of LSI-R and substance abuse assessment scores (Listwan, Borowiak, et al., 2008). For the current analysis, only high-risk drug court participants (n = 72) and high-risk probationers (n = 61) were included in the sample.

Measures

Independent variable

The primary independent variable for this study was drug court participation (group membership). Group membership (0 = probation; 1 = drug court) was explored to determine whether drug court participation reduced recidivism.

Dependent variable

The dependent variable for this study was recidivism. Recidivism was measured by court filing post-intake. This was defined as all new charges presented to the court by the prosecutor for processing (0 = no; 1 = yes). Because a court filing requires prosecutorial action, it provides a more conservative measure of recidivism than arrest and may help minimize false positives (Listwan, Borowiak, et al., 2008). Offenders were followed for an average of 927 days, with a range from 429 days to 1,458 days.

Control variables

Because this study used a quasi-experimental design, it was also important to control for several demographic factors including gender, age, race, education, and marital status that may predict recidivism. Previous research has shown that gender (Belknap & Holsinger, 2006; Butzin, Saum, & Scarpitti, 2002; Steffensmeier & Allan, 1996; Wolfe, Guydish, & Termondt, 2002) and age are significant factors in predicting recidivism (Butzin et al., 2002; Farrington, 1986; Steffensmeier, Allan, Harer, & Streifel, 1989). Gender was coded as 1 = male and 0 = female. Age was treated as a continuous variable. Prior research has also found that race is predictive of recidivism, with White offenders less likely to recidivate compared with Other offenders (Brewster, 2001; Hawkins, Laub, Lauritsten, & Cothern, 2000; Laub, 1983; Miethe, Lu, & Reese, 2000). Race was collapsed into two categories (0 = White; 1 = non-White) because the distribution across categories of race was minimal.

Prior research has found that participants with at least a high school degree are more likely to graduate from a drug court than those who do not have a high school degree (Hartley & Phillips, 2001). Education was measured by whether or not a participant graduated high school (0 = yes; 1 = no). In addition, individuals who are married have been found to be more likely to complete the program than participants who are not married (Mateyoke-Scrivner, Webster, Stanton, & Leukefeld, 2004). Marital status was measured by whether or not a participant is married (0 = married; 1 = not married). Finally, time at risk was controlled for because the length of follow-up varied. Participants with longer follow-up periods have a greater likelihood of recidivating (Banks & Gottfredson, 2004; Listwan, Sundt, Holsinger, & Latessa, 2003). Finally, time at risk was a continuous variable defined as the number of days between date of intake and the records check.

Analytical Procedures

The analysis for this study comprised of several steps. First, a bivariate analysis was conducted to gain an initial assessment of the distributions for each variable across groups. Chi-square tests were performed to test for significant differences between the groups. Multivariate analyses were used to control for factors associated with recidivism while examining the importance of drug court participation.

Results

Sample Characteristics

Table 1 describes the characteristics at intake for both groups. Drug court participants and probationers were similar in a number of areas. Though the majority of both groups were Caucasian, probationers were significantly more likely to be of minority-status. Drug court participants were significantly more likely to be women; more than half of the participants were women compared with just over a third of probationers. The groups were similar in terms of age. Most clients were between the ages of 19 and 34 with an average age of 28 for drug court participants and 29 for probationers. The groups were similar in terms of marital status and education. The vast majority of offenders were not married, but more than half of each group had at least a high school degree. Both groups were similar in their risk for recidivism. The mean LSI-R score for drug court participants was 37, whereas the mean score for probationers was 39. An individual is at high risk of recidivism, according to developers of the instrument, if the score is 34 or higher. Finally, with regard to the drug court group, approximately 47% of the drug court group graduated successfully during the study period.

Table 1.

Descriptive Statistics of Treatment and Comparison Groups.

	Treatment group		Comparison group
	n	%	n	%
Race
White	64	95.5	49	80.3
Non-White	3	4.5	12	19.7
χ² = 7.126, p = .008
Gender
Male	28	38.9	38	62.3
Female	44	61.1	23	37.7
χ² = 7.237, p = .007
Age
12-18	8	11.1	2	3.3
19-25	26	36.1	24	40.0
26-34	21	29.2	19	31.7
35-50	17	23.6	15	25.0
M (SD)	28.01 (8.75)		29.37 (9.35)
Married
Yes	13	18.8	7	11.5
No	56	81.2	54	88.5
High school graduate
Yes	45	62.5	37	60.7
No	27	37.5	24	39.3
Mean LSI-R score (SD)	37.08 (2.43)		38.95 (3.06)
Graduation status
Graduated	30	46.8	—	—
Terminated	34	53.2	—	—

Note. LSI-R = Level of Service Inventory–Revised.

Outcome Information

Of primary interest was whether high-risk drug court participants differed from high-risk probationers with regard to court filings. The results of these analyses are presented in Table 2. As indicated, drug court participants were significantly less likely to be charged with a new offense compared with probationers. Specifically, 44% of participants were rearrested compared with 56% of the comparison group. These differences are particularly salient given that the treatment group was followed for a significantly longer period of time than the probationers (1,000 days vs. 836 days, respectively). Among those rearrested, the majority, in both groups, were charged with either drug or system violations.

Table 2.

Frequency Distribution of Outcomes.

	Treatment group		Comparison group
	n	%	N	%
New court filing
Yes	33	45.8	42	56.0
No	39	54.2	11	22.0
χ² = 14.200, p = .000
Rearrest charge
Drug	11	33.3	19	45.2
DUI	3	9.1	2	4.8
Violence	3	9.1	4	9.5
Theft	7	21.2	0	0.0
System violation	9	27.3	15	35.7
Other	0	0.0	2	4.8
χ² = 12.070, p = .034
Mean days followed (SD) t = −3.304, p = .001	1,000.26 (272.16)		836.26 (300.02)

Note. DUI = driving under the influence.

In an effort to better assess the impact of the drug court, a phi coefficient was calculated as a measure of association between group membership and recidivism. The reduction in recidivism results in an effect size of .322 (CI = [.157, .470], p < .05). The Binomial Effect Size Display (BESD; Rosenthal & Rubin, 1982), used to interpret results, reveals a 34% recidivism rate among participants and a 66% recidivism rate among probationers assuming a 50% base rate.

Multivariate Analysis

A multivariate model was estimated to control for differences between the groups and to identify predictors of recidivism. Table 3 reveals the results. As indicated, the model chi-square was 19.657 (p = .006) and the Log Likelihood was 142.642. The only significant variable in the model was drug court participation. After controlling for age, race, sex, education, marital status, and follow-up period, the likelihood of recidivism increased nearly 80% for members of the comparison group compared with drug court participants.

Table 3.

Logistic Regression: Group Membership Predicting Recidivism.

	B	SE	Wald	df	Significant	Exp (B)
Drug court^a	−1.703	0.472	13.016	1	0.000	0.182
Age	−.001	0.024	0.002	1	0.963	0.999
Male	.558	0.441	1.600	1	0.206	1.748
Non-White	−.027	0.702	0.001	1	0.970	0.974
Less than HS	−.033	0.421	0.006	1	0.937	0.967
Not married	−.484	0.568	0.725	1	0.394	0.617
Days followed	.001	0.001	2.018	1	0.155	1.001
Constant	.654	1.112	0.346	1	0.556	1.924

Note. log likelihood = 142.642.

Model χ² = 19.657.

Discussion

Research suggests that treating high risk–high needs offenders is the most effective means to reduce recidivism and effective use of taxpayer dollars. The reliance on alternatives to prison will continue as state budgets demand continued decreases in correctional costs. Operationalizing best practices with regard to placement decisions is a critical need for the criminal justice system. Drug courts and probation are two leading options along the community-based continuum of supervision and services. This research sought to answer the question: is one more effective for high-risk offenders with substance abuse problems?

The findings from this study suggest that treating drug offenders in the community through an intensive service based approach is more effective than simply placing them under probation supervision. High-risk drug court participants had lower rates of recidivism than their probation counterparts and had graduation rates slightly with the program’s overall graduation rate. These findings speak to the limitations of standard probation and the efficacy of the drug court model for serving high-risk drug offenders. Probation agencies are often grappling with time constraints, high caseloads, and the involvement of multiple agencies that are not necessarily working in collaboration. The lack of coordination makes it more difficult for probation officers to respond quickly to violations with graduated sanctions (not just revocations). As noted, the wraparound service based approach offered by drug courts makes them better able to attend to the significant treatment needs presented by this population. In the absence of a proactive supervision model (Taxman, Soule, & Gelb, 1999), probation should be reserved for lower risk offenders with higher risk offenders receiving more intensive supervision and treatment.

While the drug court in this study was able to serve high-risk offenders more effectively than probation, the diversion of high-risk offenders to drug courts may meet with some resistance. Specifically, the distinction between risk of dangerousness and risk of recidivism continues to muddy the political and practice discourse (Marlowe, 2009). To state a program serves high-risk clients without building consensus on a clear definition of this concept may be politically unfeasible.

Despite political barriers to placing high-risk offenders into drug courts, the reality is that many high-risk offenders remain in the community. Community-based programs must balance the tensions between the need for intensive treatment, efficient use of resources, and public safety. Drug courts are in a position to create this balance given their structure (e.g., intensive supervision paired with treatment services) and length of services (e.g., 12-24 months on average). However, targeting low-risk offenders undermines their effectiveness. Previous research on the Ada County drug court found an overall effect size of .266; limiting drug court participation to high-risk offenders increases the effect size to .322 (Shaffer, Listwan, Kobus, & Latessa, 2010).

Using empirically supported approaches for making placement decisions can help to ensure drug courts receive clients most appropriate for their services. Although drug courts should use standardized risk/need assessment instruments, the use of actuarial tools is not a common practice in community corrections settings (Taxman, Cropsey, Young, & Wexler, 2007). Results from the National Drug Court Survey found that only 21% of adult drug court programs use risk assessment tools (Taxman & Perdoni, 2009). Research, however, has suggested that drug court programs could successfully use actuarial measures and clinical expertise to distinguish between high- and low-risk individuals and aid with program placement decisions (Guastaferro, 2012).

While the findings of this study are promising, there are several limitations to consider. First, the lack of process data available among drug court participants hinders our ability to determine which part of the drug court is most successful. Some researchers suggest that the Judge is a key component of the drug court’s success while others argue that treatment services and dosage are the backbone. We were unable to gather the type of treatment data necessary to analyze the impact of dosage or treatment type on individual behavior. Second, detailed process data on the comparison group were also unavailable. Comparing the two groups on indicators such as drug of choice, frequency of drug use, treatment service exposure, and LSI-R reassessment results would have enhanced the evaluation. Third, restricting our analysis to high-risk offenders resulted in a relatively small sample. Despite the small numbers, there was sufficient statistical power to detect significant differences between the groups. Finally, random assignment to the groups was not available. It is recognized that random assignment is often not a feasible option, however, in its absence careful consideration must be given to the selection of comparison group members. Although the groups differed in terms of race and gender, they were equivalent in terms of their propensity to reoffend as measured by the LSI-R. Therefore, we feel confident that our selection of the comparison group and the use of statistical controls have increased the probability that the outcome differences are attributable to the drug court intervention.

While the current study provides support for treating high-risk offenders in drug courts, additional research is needed on this issue. Specifically, it is important to more closely examine the nature and intensity of services offered to these offenders. While drug courts are theoretically intensive programs, the existing research suggests there is great variation in the model across sites and relatively little is known about the services provided to participants (Shaffer, 2011). Additional research is necessary to examine the degree to which other drug courts are reaching high-risk populations and to what extent their outcomes vary by the duration and intensity of services.

In summary, drug courts have received hard-earned support from policy makers and practitioners. Indeed, recent criminal justice reforms have identified an increased reliance on drug courts as an important contributor to improving criminal justice outcomes in a cost-efficient manner (e.g., see the Public Safety Performance Initiative, Pew Center on the States). If these reform policies are implemented as intended, the pressure to deliver positive outcomes for more high-risk individuals will increase. In other words, if states truly divert people who would otherwise be going to prison, drug courts will work with higher risk individuals in larger numbers. Developing the capacity of drug courts to address the needs of this population is paramount. The findings presented here support the practice of accepting high-risk offenders. In fact, high-risk offenders in drug court had significantly lower recidivism rates than similar offenders on probation. Furthermore, the findings of this study cast doubt on the practice of enrolling first-time offenders in drug courts. Drug courts are one option on the criminal justice response continuum and should be reserved for individuals in need of intensive treatment and supervision.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was funded by the Idaho Supreme Court and the Substance Abuse and Mental Health Administration (Grant CFDA 92.243). The grant funded the original data collection for a statewide evaluation and was not directly related to the current analysis/study.

Notes

References

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.

Andrews

(1995). The psychology of criminal conduct and effective treatment. In McGuire

(Ed.), What works: Reducing reoffending, guidelines from research and practice (pp. 35-62). New York, NY: John Wiley.

Andrews

D. A.

Bonta

(2010). The psychology of criminal conduct (5th ed.). New Providence, NJ: LexisNexis.

Andrews

D. A.

Zinger

Hodge

R. D.

Bonta

Gendreau

Cullen

F. T.

(1990). Does correctional treatment work? A clinically relevant and physiologically informed meta-analysis. Criminology, 28, 369-404.

Andrews

Bonta

(1995). LSI-R: The level of service inventory-revised. Toronto, Ontario: Multi-Health Systems.

Aos

Phipps

Barnoski

Lieb

(2001). The comparative costs and benefits of programs to reduce crime. Olympia: Washington State Institute for Public Policy.

Austin

(2004). The proper and improper use of risk assessment in corrections. Federal Sentencing Reporter, 16, 194-199.

Babbie

(2013). The practice of social research. Belmont, CA: Wadsworth.

Banks

Gottfredson

D. C.

(2004). Participation in drug treatment court and time to rearrest. Justice Quarterly, 21, 637-658.

10.

Belenko

(2002a). The challenge of conducting research in drug treatment court settings. Substance Use & Misuse, 37, 1635-1664.

11.

Belenko

(2002b). Drug courts. In Leukefeld

C. G.

Tims

Farabee

(Eds.), Treatment of drug offenders: Policies and issues (pp. 301-318). New York, NY: Springer Publishing.

12.

Belknap

Holsinger

(2006). The gendered nature of risk factors for delinquency. Feminist Criminology, 1, 48-71.

13.

Brewster

M. P.

(2001). An evaluation of the Chester County (PA) drug court program. Journal of Drug Issues, 31, 177-206.

14.

Brocato

Wagner

E. F.

(2008). Predictors of retention in an alternative-to-prison substance abuse treatment program. Criminal Justice and Behavior, 35, 99-119.

15.

Butzin

C. A.

Saum

C. A.

Scarpitti

F. R.

(2002). Factors associated with a drug treatment court diversion program. Substance Use & Misuse, 37, 1615-1633.

16.

California Department of Alcohol and Drug Programs. (2006). Fact sheet: Substance Abuse and Crime Prevention Act of 2000. Sacramento, CA: Office of Criminal Justice Collaboration. Retrieved from cadpaac.org/downloads/SACPA%20Fact%20Sheet.pdf

17.

Campbell

D. T.

Stanley

J. C.

(1963). Experimental and quasi-experimental designs for research. Boston, MA: Houghton Mifflin Company.

18.

Chandler

R. K.

Fletcher

B. W.

Volkow

N. D.

(2009). Treating drug abuse and addiction in the criminal justice system: Improving public health and safety. The Journal of the American Medical Association, 301, 183-190.

19.

Dowden

Andrews

D. A.

(1999). What works for female offenders: A meta-analytic review. Crime & Delinquency, 45, 438-452.

20.

Dowden

Andrews

D. A.

(2000). Effective correctional treatment and violent reoffending. Canadian Journal of Criminology, 42, 449-467.

21.

Dowden

Andrews

D. A.

(2004). The importance of staff practice in delivering effective correctional treatment: A meta-analytic review of core correctional practice. International Journal of Offender Therapy and Comparative Criminology, 48, 203-214.

22.

Drug Strategies. (1997). Cutting crime: Drug courts in action. Washington, DC: Author. Retrieved from http://www.drugstrategies.com/pdf/CuttingCrime97.pdf

23.

Farrington

D. P.

(1986). Age and crime. Crime and Justice, 7, 189-250.

24.

Festinger

D. S.

Marlowe

D. B.

Lee

P. A.

Kirby

K. C.

Bovasso

McLellan

A. T.

(2002). Status hearings in drug court: When more is less and less is more. Drug and Alcohol Dependence, 68, 151-157.

25.

Flores

A. W.

Lowenkamp

C. T.

Smith

Latessa

E. J.

(2006). Validating the Level of Service Inventory-Revised on a sample of federal probationers. Federal Probation, 70, 44-48.

26.

Gendreau

Little

Goggin

(1996). A meta-analysis of the predictors of adult offender recidivism: What works! Criminology, 34, 575-607.

27.

Gottfredson

S. D.

Moriarty

L. J.

(2006). Clinical versus actuarial judgments in criminal justice decisions: Should one replace the other? Federal Probation, 70(2), 15-18.

28.

Government Accountability Office. (2011). Adult drug courts: Studies show courts reduce recidivism, but DOJ could enhance future performance measure revisions efforts (GAO-12-53). Retrieved from http://www.gao.gov/assets/590/586793.pdf

29.

Greene

J. A.

(2003). Smart on crime: Positive trends in state-level sentencing and corrections policy. Washington, DC: Families Against Mandatory Minimums.

30.

Greene

J. A.

Schiraldi

(2002). Cutting correctly: New prison policies for times of fiscal crisis. San Francisco, CA: Center on Juvenile and Criminal Justice. Retrieved from http://www.justicepolicy.org/uploads/justicepolicy/documents/02-02_rep_cuttingcorrectly_ac.pdf

31.

Guastaferro

W. P.

(2012). Using the Level of Service Inventory-Revised to improve assessment in drug court. International Journal of Offender Therapy and Comparative Criminology, 56, 769-789. doi:10.1177/0306624X11413879

32.

Hartley

R. E.

Phillips

R. C.

(2001). Who graduates from drug courts? Correlates of client success. American Journal of Criminal Justice, 26, 107-119.

33.

Hawkins

D. F.

Laub

J. H.

Lauritsten

J. L.

Cothern

(2000). Race, ethnicity, and serious violent juvenile offending. Office of Juvenile Justice and Delinquency Prevention. Retrieved from http://www.ncjrs.gov/pdffiles1/ojjdp/181202.pdf

34.

Holsinger

A. M.

Lowenkamp

C. T.

Latessa

E. J.

(2006). Exploring the validity of the Level of Service Inventory-Revised with Native American offenders. Journal of Criminal Justice, 34, 331-337.

35.

Hubbard

R. L.

Craddock

S. G.

Anderson

(2003). Overview of 5-year followup outcomes in the drug abuse treatment outcome studies (DATOS). Journal of Substance Abuse Treatment, 25, 125-134.

36.

Hughes

Wilson

D. J.

Beck

A. J.

(2001). Trends in state parole, 1990-2000. Washington, DC: Bureau of Justice Statistics.

37.

Kelly

C. E.

Welsh

W. N.

(2008). The predictive validity of the level of service inventory-revised for drug involved offenders. Criminal Justice and Behavior, 35, 819-832.

38.

Kleiman

M. A.

Tran

T. H.

Fishbein

Magula

Allen

Lacy

(2003). Opportunities and barriers in probation reform: A case study of drug testing and sanctions. Berkeley, CA: California Policy Research Center, University of California. Retrieved from http://escholarship.org/uc/item/0238v37t#page-2

39.

Laub

J. H.

(1983). Urbanism, race and crime. Journal of Research in Crime & Delinquency, 20, 183-198.

40.

Listwan

S. J.

Borowiak

Latessa

E. J.

(2008). The impact of felony drug courts in Idaho: Findings and recommendations (Technical Report). Cincinnati, OH: University of Cincinnati Division of Criminal Justice.

41.

Listwan

S. J.

Jonson

S. L.

Cullen

F. T.

Latessa

E. J.

(2008). Cracks in the penal harm movement: Evidence from the field. Criminology & Public Policy, 7, 423-465.

42.

Listwan

S. J.

Latessa

E. J.

(2003). Ada and Kootenai county drug courts: Outcome evaluation findings (Technical Report). Cincinnati, OH: University of Cincinnati Division of Criminal Justice.

43.

Listwan

S. J.

Shaffer

D. K.

Latessa

E. J.

(2002). The drug court movement: Recommendations for improvement. Corrections Today, 64(5), 52-54, 120, 123.

44.

Listwan

S. J.

Sundt

J. L.

Holsinger

A. M.

Latessa

E. J.

(2003). The effect of drug court programming on recidivism: The Cincinnati experience. Crime & Delinquency, 49, 389-411.

45.

Longshore

Hawken

Urada

Anglin

M. D.

(2006). Evaluation of the Substance Abuse and Crime Prevention Act: SACPA Cost-Analysis Report (first and second years). Los Angeles: Department of Alcohol and Drug Programs, California Health and Human Services Agency, University of California, Los Angeles Integrated Substance Abuse Programs.

46.

Lowenkamp

C. T.

Holsinger

A. M.

Latessa

E. J.

(2001). Risk/need assessment, offender classification and the role of child abuse. Criminal Justice and Behavior, 28, 543-563.

47.

Lowenkamp

C. T.

Holsinger

A. M.

Latessa

E. J.

(2005). Are drug courts effective? A meta-analytic review. Journal of Community Corrections, 28, 5-10.

48.

Lowenkamp

C. T.

Latessa

E. J.

(2004). Understanding the risk principle: How and why correctional interventions can harm low-risk offenders. In Topics in community corrections (pp. 3-8). Washington, DC: Department of Justice. Retrieved from http://www.uc.edu/content/dam/uc/ccjr/docs/articles/ticc04_final_complete.pdf

49.

Lowenkamp

C. T.

Latessa

E. J.

Smith

(2006). Does correctional program quality really matter? The impact of adhering to the principles of effective intervention. Criminology & Public Policy, 5, 575-594.

50.

Lurigio

A. J.

(2000). Drug treatment availability and effectiveness: Studies of the general and criminal justice populations. Criminal Justice and Behavior, 27, 495-528.

51.

Marlowe

(2009). Evidence-based sentencing for drug offenders: An analysis of prognostic risks and criminogenic needs. Chapman Journal of Criminal Justice, 1(1), 167-201.

52.

Marlowe

D. B.

DeMatteo

D. S.

Festinger

D. S.

(2003). A sober assessment of drug courts. Federal Sentencing Reporter, 16, 153-157.

53.

Marlowe

D. B.

Festinger

D. S.

Lee

P. A.

Dugosh

K. L.

Benasutti

K. M.

(2006). Matching judicial supervision to clients’ risk status in drug court. Crime & Delinquency, 52, 52-76.

54.

Mateyoke-Scrivner

Webster

J. M.

Stanton

Leukefeld

(2004). Treatment retention predictors of drug court participants in a rural state. The American Journal of Drug and Alcohol Abuse, 30, 605-625.

55.

Miethe

T. D.

Reese

(2000). Reintegrative shaming and recidivism risks in drug court: Explanations for some unexpected findings. Crime & Delinquency, 46, 522-541.

56.

Mitchell

Wilson

D. B.

Eggers

MacKenzie

(2012). Assessing the effectiveness of drug courts on recidivism: A meta-analytic review of traditional and non-traditional drug courts. Journal of Criminal Justice, 40, 60-71.

57.

Mumola

C. J.

Karberg

J. C.

(2006). Drug use and dependence, state and federal prisoners, 2004. Washington, DC: Office of Justice Programs, Bureau of Justice Statistics.

58.

National Association of Drug Court Professionals. (2012). Drug court history and types of drug courts. Retrieved from http://www.nadcp.org/learn/what-are-drug-courts/drug-court-history

59.

New York State Department of Correctional Services. (2004). Pataki signs rocky drug reform into law. Docs Today, 13, 4-5.

60.

Peters

R. H.

Murrin

M. R.

(2000). Effectiveness of treatment-based drug courts in reducing recidivism. Criminal Justice and Behavior, 27, 72-96.

61.

Pew Center on the States. (2009). One in 31: The long reach of American corrections. Washington, DC: The Pew Charitable Trusts.

62.

Rempel

Fox-Kralstein

Cissner

Cohen

Labriola

Farole

. . . Magnani

(2003). The New York State adult drug court evaluation: Policies, participants and impacts. New York, NY: Center for Court Innovation.

63.

Rosenthal

J. A.

Rubin

D. B.

(1982). A simple, general purpose display of magnitude of experimental effect. Journal of Educational Psychology, 74, 166-169.

64.

Rusche

(2000). California proposition 36. Atlanta, GA: National Families in Action. Retrieved from nationalfamilies.org/guide/california36-full.html

65.

Schlager

M. D.

Simourd

D. J.

(2007). Validity of the level of service inventory revised (LSI-R) among African American and Hispanic male offenders. Criminal Justice and Behavior, 34, 545-555.

66.

Shaffer

D. K.

(2006). Reconsidering drug court effectiveness: A meta-analytic review (AAT No. 3231113). Dissertation Abstracts International, 67, 09A.

67.

Shaffer

D. K.

(2011). Looking inside the black box of drug courts: A meta-analytic review. Justice Quarterly, 28, 493-521.

68.

Shaffer

D. K.

Hartman

J. A.

Listwan

S. J.

(2009). The efficacy of drug courts: Does gender matter? Journal of Drug Issues, 39, 1045-1069.

69.

Shaffer

D. K.

Listwan

S. J.

Kobus

K. D.

Latessa

E. J.

(2010). Examining the impact of drug court participation for moderate and high risk offenders. Paper presented at the Annual Meeting of the Academy of Criminal Justice Sciences, San Diego, CA.

70.

Simourd

D. J.

Malcom

P. B.

(1998). Reliability and validity of the Level of Service Inventory-Revised among federally incarcerated sex offenders. Journal of Interpersonal Violence, 13, 261-274.

71.

Simpson

D. D.

Joe

G. W.

Broome

K. M.

Hiller

M. L.

Knight

Rowan-Szal

G. A.

(1997). Program diversity and treatment retention rates in the Drug Abuse Treatment Outcome Study (DATOS). Psychology of Addictive Behaviors, 11, 279-293.

72.

Simpson

D. D.

Joe

G. W.

Brown

B. S.

(1997). Treatment retention and follow-up outcomes in the Drug Abuse Treatment Outcome Study (DATOS). Psychology of Addictive Behaviors, 11, 294-307.

73.

Spohn

Holleran

(2002). The effect of imprisonment on recidivism rates of felony offenders: A focus on drug offenders. Criminology, 40, 320-357.

74.

Steffensmeier

Allan

(1996). Gender and crime: Toward a gendered theory of female offending. Annual Review of Sociology, 22, 459-487.

75.

Steffensmeier

D. J.

Allan

E. A.

Harer

M. D.

Streifel

(1989). Age and the distribution of crime. Journal of Sociology, 94, 803-831.

76.

Taxman

F. S.

Bouffard

J. A.

(2003). Drug treatment in the community: A case study of system integration issues. Federal Probation, 67(2), 4-26.

77.

Taxman

F. S.

Cropsey

K. L.

Young

D. W.

Wexler

(2007). Screening, assessment, and referral practices in adult correctional settings: A national perspective. Criminal Justice and Behavior, 34, 1216-1234.

78.

Taxman

F. S.

Perdoni

(2009). Drug courts. In Mitchell Miller

(Ed.), 21st century reference series (Vol. 2., pp. 675-686). Thousand Oaks, CA: SAGE.

79.

Taxman

F. S.

Soule

Gelb

(1999). Graduated sanctions: Stepping into accountable systems and offenders. The Prison Journal, 79, 182-204.

80.

Vieira

T. A.

Skilling

T. A.

Peterson-Baldali

(2009). Matching court-ordered services with treatment needs: Predicting treatment success with young offenders. Criminal Justice and Behavior, 36, 385-401.

81.

Warren

(2008). One in 100: Behind bars in America. Washington, DC: The Pew Charitable Trusts.

82.

Wilson

D. B.

Mitchell

Mackenzie

D. L.

(2006). A systematic review of drug court effects on recidivism. Journal of Experimental Criminology, 2, 459-487.

83.

Wittman

F. D.

(2001). Prevention, community services, and Proposition 36. Journal of Psychoactive Drugs, 33, 343-352.

84.

Wolfe

Guydish

Termondt

(2002). A drug court outcome evaluation comparing arrests in a two-year follow-up period. Journal of Drug Issues, 32, 1155-1172.

85.

Wolff

Pogorzelski

(2005). Measuring the effectiveness of mental health courts: Challenges and recommendations. Psychology, Public Policy, and Law, 11, 539-569.