• Journal Article

Predicting the effect of substance abuse treatment on probationer recidivism


Lattimore, P., Krebs, C., Koetse, W., Lindquist, C., & Cowell, A. (2005). Predicting the effect of substance abuse treatment on probationer recidivism. Journal of Experimental Criminology, 1(2), 159-189.


Support for the effectiveness of substance abuse treatment to reduce substance use and recidivism among populations supervised by the criminal justice system continues to grow in substance abuse and criminal justice literature. Recent studies show that a variety of programs including the Breaking the Cycle program and drug courts appear to result in improved outcomes for offenders. In this paper, we examine the effect of non-residential substance abuse treatment on arrest. Our data are for almost 134,000 ‘drug-involved’ individuals sentenced to probation in Florida between July 1995 and June 2000. Nearly 52,000 of these individuals received non-residential substance abuse treatment, while 81,797 did not. Our approach is a methodologically simple one that entails stratifying our data by treatment status, estimating logit and negative binomial models of arrest for each of the two datasets, and then applying each model to both datasets. This approach, which requires that both groups include subjects for whom treatment is appropriate, is analogous to using regression models to predict outcomes for new values of independent variables. For each observation in the dataset, we use the models to predict the expected outcomes for each individual under two scenarios – receiving non-residential treatment and receiving no treatment. Summing over these individual estimates provides an estimate of the total numbers of arrests that would be expected under different levels of population exposure to treatment. Results suggest that non-residential treatment reduced both the expected numbers of individuals who recidivated (i.e., were arrested) and the expected total numbers of arrests in the 12 and 24 months following placement on supervision.