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Drug Court Participation and Time to Failure: an Examination of Recidivism Across Program Outcome

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Abstract

Drug courts were developed to offer substance abuse treatment along with intensive supervision in an effort to better attend to the needs of these offenders, lessen commitments to prison, and reduce costs to the criminal justice system. Despite the reported success of drug courts, reductions in recidivism appear to be reserved for those who complete the program. Those who fail the program are remanded back to the court for traditional sentencing that may negate any participation benefit. Scholars have long considered the role the criminal justice system has played in the desistance of criminal activity. Much of the research has focused on the outcomes of postconviction sanctioning, finding little support for incarceration has as a deterrent agent. Moreover, the stigma of a criminal conviction, alone, has been shown to exacerbate criminal offending. We used a sample of 733 drug court participants to compare reoffending patterns between sentencing outcomes (dismissal, failed-probation, failed incarcerated). We used survival analysis to compare criminal abstinence in drug court participants across three potential program outcomes – case dismissal, probation, and imprisonment. The current findings demonstrate differences in recidivism between convicted and non-convicted past participants, but see mostly null effects when isolating the analysis between custodial and non-custodial sentences.

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Notes

  1. For drug court failures sentenced to probation, a recidivism event was only scored if the offender was convicted of a new offense. Technical violations leading to probation revocation were excluded as recidivism events.

  2. It should be noted that 10 of these observations were sentenced to jail with credit for time served. These individuals served no time in jail or prison following the completion of the drug court. From a theoretical standpoint, we felt these individuals were more appropriately coded under probation.

  3. We used a hierarchy rule to classify each observation’s convicted charge. In instances where a participant was charged with multiple offenses, we recorded the participant’s most serious offense.

  4. The term “hazardous drug” is designated by the state in which this drug court is located. Hazardous drugs include illicit substances, such as cocaine, heroin, methamphetamine, unprescribed medication, etc.

  5. We initially included a measure for the length of incarceration; however, we found no relationship between this variable and risk for incarcerated samples. Moreover, two of our outcome groups were not incarcerated. Due to the lack of utility length of incarceration represented in the model, we removed this measure from our final analysis.

  6. Due to the recent popularity of PSAs, we encourage the reader to refer to the wealth of literature detailing the utility, preparation, procedure, and limitations of PSAs for more information about these models (Guo & Fraser, 2010; King & Nielsen, 2019; Loughran, Wilson, Nagin, & Piquero, 2015; Sampson et al., 2006).

  7. Probit regression, rather than logistic regression, was used to calculate the propensity scores for both the PSM and IPTW analyses. In the instance of the PSM, the psmatch2 command in Stata uses a probit regression as its default for propensity scores. We employed a multinomial probit regression when calculating the propensity scores in our IPTW analyses. The use of probit regression in this instance is advised within the sentencing literature (Bushway, Johnson, & Slocum, 2007; Koons-Witt, Sevigny, Burrow, & Hester, 2014). In a two-part sentencing model, the initial in/out decision is more appropriately specified with a probit model, as these models assume normality, rather than log normality, as its functional form (Bushway et al., 2007). The multinomial probit model predicting case outcome in the IPTW analysis mirrors that of the in/out decision in common sentencing literature. Moreover, the four attributes of the case outcome variable dictated the use of a multinomial model. Subsequently, it was decided that a multinomial probit model is the most appropriate method to calculate our propensity scores.

  8. A post-matching sensitivity analysis using Mantel-Haenszel bounds was run to determine the sensitivity of our matching process to confoundedness using the mhbounds command in Stata 15. The odds of differential assignment based on unobserved factors (referred to as gamma) was estimated to be 1.2. This means that an unobserved variable would have to change our predicted model for treatment assignment in the propensity score estimation analysis by a factor of 1.2.

  9. It is common to compute Martingale residuals to assess potential transformations for time-varying covariates in survival analysis (Allison, 2014). As our analysis did not include any time-varying covariates, these residuals were not included in our diagnostic procedures.

  10. In our sample, there were four observations that, strangely, had their cases dismissed despite their failure to complete treatment.

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Gibbs, B.R., Lytle, R. Drug Court Participation and Time to Failure: an Examination of Recidivism Across Program Outcome. Am J Crim Just 45, 215–235 (2020). https://doi.org/10.1007/s12103-019-09498-0

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