Drug Courts: Effectiveness, Limitations, and Reform Potential

Overview of Drug Court Effectiveness

It has taken nearly two decades for consensus to solidify, but most authors now agree that drug courts reduce recidivism and long-term social cost. Huddleston, Marlowe, and Casebolt argue that "no other justice intervention can rival the results produced by drug courts" (2008, p. 2). Drug courts are the most cost-effective method we have found for improving addiction treatment outcomes and reducing crime (Huddleston, Marlowe, and Casebolt, 2008, p. 5). Congress agrees and asserts that the economic benefits very likely far exceed what can be tallied directly (United States Government Accountability Office, 2005, p. 74). This broad consensus, however, masks significant differences both within and between programs. Not all drug court programs are the same, and not all offenders respond the same way within the same drug court (Roman, Townsend, and Bhati, 2003, p. 1).

As of 2008, 59% of drug courts were post-conviction; 7% were diversionary, pre-plea models; and 19% were hybrids of both (Huddleston, Marlowe, and Casebolt, 2008, p. 4). In total, 78% of the 1,174 U.S. drug courts at that time had post-plea or probationary programs. Huddleston, Marlowe, and Casebolt conclude that this implies a growing trend toward focusing on "higher risk and higher need offender" populations (2008, p. 5). Programs have been studied widely enough that cross-study meta-analysis is beginning to deliver "definitive evidence" for both the effectiveness and cost savings of drug court programs (Huddleston, Marlowe, and Casebolt, 2008, p. 5). The consensus is that drug courts reduce crime rates by 7–14% on average, with high results reaching 35% in crime reduction (Huddleston, Marlowe, and Casebolt, 2008, p. 5). The United States Government Accountability Office (GAO) found an average of 10–30% less recidivism for drug court participants compared to a control group of non-drug court offenders, with similar duration effects sustained over time (2005, pp. 45–46). Drug court clients also went longer before re-arrest and had lower re-conviction rates than similar offenders processed through traditional courts (United States Government Accountability Office, 2005, p. 49).

What these results ultimately reveal, however, is the wide variation within these broader claims of success. Differences arise in how drug court systems measure recidivism — whether by re-arrest, conviction, or reincarceration — and different offender demographics and eligibility characteristics are often bundled into aggregate success rates (Roman, Townsend, and Bhati, 2003, pp. 12–13). While program completion generally indicates lower recidivism rates, and compliance with program procedures indicates likelihood of completion, specific program components vary so widely and have such varying effects that no particular component emerges as the single best design element beyond compliance with supervision mandates (United States Government Accountability Office, 2005, p. 49). The magic bullet for reducing either recidivism or relapse has remained elusive because of the wide divergence between programs, which creates barriers to robust analysis.

Program Variation and Geographic Differences

The pattern of drug abuse presenting in courts has been found to vary across rural, suburban, and urban locations. Rural drug courts see more methamphetamine arrests; marijuana and alcohol are the primary problem substances in suburban areas; and crack cocaine predominates in urban courts in 74% of states (Huddleston, Marlowe, and Casebolt, 2008, p. 8). This is significant because these different drug profiles are associated with varying completion rates within and across different courts. Recidivism rates depend on which drug violations are identified as primary, correlated to the concentration of cocaine and heroin addiction compared to alcohol and marijuana offenses, rather than on individual drug court recidivism rates across uniform proportions of each substance (Roman, Townsend, and Bhati, 2003, pp. 5–6).

These averages mask important differences between drug courts. The majority of courts clustered near the average, with the highest recidivism rates found where courts served the most "difficult to reach" clients — reflecting the challenge of the caseload rather than weak performance for a given court across identical populations (Roman, Townsend, and Bhati, 2003, p. 5). Drug courts handling higher absolute volume also experienced significantly lower performance as measured by recidivism than smaller courts, by up to 8% after two years (Roman, Townsend, and Bhati, 2003, p. 6). The largest courts are located in the largest cities, which also have the highest severity of abuse (Roman, Townsend, and Bhati, 2003, p. 7). Higher-risk offenders are beginning to emerge as the most likely to show improvement compared to other drug court clients and similar conventional-court drug offenders (Huddleston, Marlowe, and Casebolt, 2008, p. 5). Higher-risk, more chronic drug offenders with longer histories of abuse have been found to achieve better drug court outcomes the more frequently their required judicial hearings occurred (Huddleston, Marlowe, and Casebolt, 2008, p. 4).

The rapidly growing numbers of methamphetamine offenders display both of these characteristics: rural concentration and more persistent, criminally entrenched dependence requiring longer and more intensive intervention (Huddleston, Marlowe, and Casebolt, 2008, p. 16). Drug courts appear to be the most successful correctional tool ever employed for reducing long-term methamphetamine addiction (Huddleston, Marlowe, and Casebolt, 2008, p. 16).

Demographic Disparities in Drug Court Outcomes

Clients with different demographic characteristics succeed at different rates even within the same severity classifications. Roman, Townsend, and Bhati find significant differences across gender, race, ethnicity, and age groups in all study courts (2003, p. 7). Women have higher success rates than men; White participants are re-arrested less often than non-Black minorities, who in turn experience lower recidivism rates than Black participants (Roman, Townsend, and Bhati, 2003, p. 7). Age also correlates with higher success rates, though with wide dispersion: the youngest offenders demonstrate significantly worse performance than the median age cohort, while the oldest cohorts score far higher than the median. The GAO research supports these findings (2005, p. 69).

These results, however, conceal meaningful differences in eligibility characteristics and reporting practices across different courts, and the authors caution against directly comparing reported outcomes across jurisdictions (Roman, Townsend, and Bhati, 2003, p. 7). The unexplained persistence of high recidivism rates for specific racial groups — far beyond their proportional representation in the total demographic — may have deeper causes that are not easily addressed through program design alone (Finigan, 2009, p. 140).

Conclusion

Drug courts lower recidivism rates compared to similar conventional trial processes far better than any other program implemented before or since (Huddleston, Marlowe, and Casebolt, 2008, p. 5). The effects on relapse remain contradictory and uncertain (United States Government Accountability Office, 2005, p. 60). Differences between offenders, between similar offenders at different sites, and between program components and their implementation hinder the identification of a successful "one size fits all" model that would support more accurate evaluation and, in turn, expanded funding (Goldkamp, 1999, p. 176). Model best practices have existed for decades, but practices within, between, and across drug court programs may never become entirely uniform because many other factors — beyond the drug court itself — contribute to and affect local outcomes. Nonetheless, site-specific information continues to improve, and successful programs continue to emerge, even if some may never be replicable beyond a specific jurisdiction.