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Despite a growing consensus among scholars that substance abuse treatment is effective in reducing recidivism, strict eligibility rules have limited the impact of current models of therapeutic jurisprudence on public safety. This research effort was aimed at providing policy makers some guidance on whether expanding this model to more drug-involved offenders is cost-beneficial. We find that roughly 1.5 million arrestees who are probably guilty (the population most likely to participate in court monitored substance abuse treatment) are currently at risk of drug dependence or abuse and that several million crimes could be averted if current eligibility limitations were suspended and all at-risk arrestees were treated.
The primary goal of this research is to determine the size of the drug-involved
offender population that could be served effectively and efficiently by partnerships
between courts and treatment. Despite a growing consensus that substance
abuse treatment promotes desistance, few arrestees receive sufficient treatment
through the criminal justice system to reduce their offending. Strict eligibility
rules and scarce resources limit access to treatment, even in jurisdictions that
embrace the principles of therapeutic jurisprudence. The result is that existing
linkages between the criminal justice system and substance abuse treatment are
so constrained that at best only small reductions in crime can be achieved.
The limited access to treatment for the criminal offender population appears
to be based on subjective judgments of both the risks of treating offenders in
the community and the benefits of treatment. Risks are assumed to be high
for most offenders, and the benefits of treatment are assumed to be low. As a
result, almost all drug-involved arrestees are determined to be ineligible for participation
in community-based treatment programs. An important question for
the nation's drug policymakers is whether a substantial expansion of substance
abuse treatment would yield benefits from reduced crime and improved public
safety. A related question is whether evidence-based strategies can be developed
to prioritize participation, given limited resources.
Previous efforts to address these questions have been constrained by limitations
in extant data, as well as by substantial econometric challenges to combining
those data. The development of new modeling techniques now allows disparate data sources to be combined and critical values to be imputed for missing
observations. Thus, in this paper we are able to combine data from several
prior studies to simulate the effects of treatment on an untreated cohort of arrestees.
That is, using existing data on the drug use patterns of arrestees and
data predicting outcomes from the treatment of similar cohorts, we are able to
predict not only how much crime could be prevented through treatment of an
arrestee population, but also which arrestees (based on their attributes) are the
best (and worst) candidates for community-based treatment.
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