This article was originally published on June 1, 2020.
In October 2019, we attended the meeting of a jail
overcrowding taskforce in Indiana where members brainstormed
solutions for jail overcrowding. A statement from an officer at the
meeting is central to the issue of jail overcrowding:
Jails serve as a key component of public safety by
incarcerating violent criminals and punishing offenders for crimes
against society, but many jails today are operating with limited
resources and are reporting concerns of overcrowding (Galvin, 2017).
The reality is that a large percentage of the
justice-involved population are so called “super-utilizers,” or
individuals who habitually reuse criminal justice resources.
Super-utilizers suffer from high rates of mental health issues,
substance use disorders, and residential instability. They typically
commit low-level, non-violent misdemeanors. As a result, jails
become a revolving door. Super-utilizers cycle in and out of jails,
and multiple short stays in jail each year becomes normal. By
identifying and diverting super-utilizers to more targeted treatment
options, states can save money, reduce jail overcrowding, improve
individual health, and ensure public safety.
There are a variety of factors that lead toward certain
individuals repeatedly cycling in and out of incarceration:
Economic and social disadvantage
A focus on public-order offenses and
de-institutionalization of mental institutions
A lack of available access to mental health and
substance abuse services for at risk populations
A lack of understanding and training regarding
the co-occurrence of mental health and substance use
A lack of resources for
identifying at risk populations for first responders and
A failure of treatment diversion
for at risk populations
The current criminal justice system does not fully address
the complex social needs of super-utilizers, so they continue to
recidivate at high rates. A solution is needed to help us identify
super-utilizers and divert them to treatment.
Using near real-time nationwide incarceration data provide
by the TotalVerify™ data hub from Equifax, data scientists
developed a proof-of-concept data-driven model that identifies
super-utilizers based on their incarceration history. Our model
looked at the number of incarcerations, recent accelerations in
incarceration activity, and the types of offenses committed, among
Using these data points super-utilizers were assigned an
indicator designating the likelihood of required intervention. This
indicator would give states the ability to focus treatment diversion
efforts on the most at-risk individuals.
Using this model, it was determined that 4-5% of
incarcerated persons are at “high risk” for super-utilizer behavior.
After identifying super-utilizers, the next step is to
connect them with available and appropriate treatment options.
Treatment offers a viable alternative for interrupting the
co-occurring substance abuse and mental health criminal justice cycle.
Research suggests that offenders with substance abuse
disorders who go untreated have a greater likelihood of recidivism
and relapsing. According to Huebner and Cobbina (2007),
probationers who failed to complete substance treatment were the
most likely to return to criminal behavior. Probationers who did
not complete treatment were 1.69 times more likely to be
re-arrested for any crime and two times more likely to be
re-arrested for a drug offense.
Digital referral platforms can unify and track behavioral
health services and connect super-utilizers to available treatment
resources and support services in real-time before or after
incarceration.Once super-utilizers are identified, treatment
diversion can be applied at two junctures in the criminal justice system:
First responders can assess via super-utilizer
indicators, exercise discretion, and divert super-utilizers to
treatment instead of incarceration.
can be provided as part of re-entry and continuity of care.
What makes treatment diversion successful? Diverting at-risk
individuals from the criminal justice system to treatment options
works in five key ways:
Treatment for offenders with co-occurring mental
health and substance use disorders should have integrated
treatment that is tailored to fit their needs.
Treatment should include skill building aimed at
preventing future offenses and drug abuse, with referral
options for vocational counseling opportunities in the
Treatment should be long
enough to address and treat substance abuse and mental
Treatment should include
a support network as part of the continuity of care for
offenders as they transition back into the community.
Treatment should use medication assisted treatment when it
meets clinically recognized standards.
One ROI assessment of treatment diversion in Nueces County,
Texas estimated that diverting people with mental illness from
arrest to treatment over a nine-month period has saved Nueces County
$2.3 million (Dearman, 2019). In another study of Santa Fe, N.M.,
the Law Enforcement Assisted Diversion program (LEAD) has saved 47%
by diverting offenders to treatment.
According to McNiel and Binder (2007), the criminal justice
system can save $47,000 for each nonviolent felony drug offender
diverted into treatment. A study by Zarkin, et al. (2012), revealed
that 10% of eligible offenders were diverted to treatment rather
than prison, the criminal justice system would save $4.8 billion
when compared to current practices.
Identifying and diverting super-utilizers has wide-ranging
policy implications for managing jail inmates before and after
release with co-occurring mental health and substance use disorders.
Jails are the point of entry in the behavioral health care system
for many people with co-occurring disorders. Investing in
assessments and treatment plans coupled with post-release community
mental health services is vital to individual health and public safety.
For offenders whose criminal behavior is linked to their
substance abuse, mental health issues and residential instability,
continual incarceration may not be an effective response for
intervention. Given the need to address super-utilizers, the
criminal justice system provides a unique opportunity to help break
the cycle of incarceration with the potential for improving public
health and safety.
Dearman, E. (2019). Mental health center: Diversion efforts
for mental health can save money. Corpus Christie Caller Times,
Retrieved from: https://www.caller.com/story/news/crime/2019/06/11/mental-health-center-diversion-mental-health-can-save-money/1409462001.
Galvin, G. (2017). Underfunded, overcrowded states prisons
struggle with reform. US News, Retrieved from: https://www.usnews.com/news/best-states/articles/2017-07-26/understaffed-and-overcrowded-state-prisons-crippled-by-budget-constraints-bad-leadership.
Huebner, B. M., & Cobbina, J. (2007). The effect of drug
use, drug treatment, participation, and treatment completion on
probationer recidivism. Journal of Drug Issues, 7(3): 619-642.
McNiel, D. E. & Binder, R. L. (2007). Effectiveness of a
mental health court in reducing criminal recidivism and violence.
The American Journal of Psychiatry, Retrieved from: https://ajp.psychiatryonline.org/doi/full/10.1176/appi.ajp.2007.06101664.
The Sentencing Project (2015). U.S. prison population trends
1999-2014: Broad variation among states in recent year. Washington DC.
Zarkin G.A., Cowell, A.J., Hicks, K.A., Mills, M.J.,
Belenko, S., Dunlap, L.J., & Keyes, V. (2012). Lifetime
benefits and costs of diverting substance-abusing offenders from
state Prison. Crime & Delinquency, 61(6), 829-850.
Daniel Downs, Ph.D., Senior Statistical Criminologist
Dr. Daniel Downs is the Lead Data Scientist for Appriss
Insights, with more than 10 years of experience in modeling and
developing analytical solutions. Daniel is responsible for crime
analytics, R&D, and applying advanced statistical techniques.
Daniel holds a Ph.D. in Criminology and Law from the University of
Illinois and is co-author of the how-to handbook for analytics in
retail loss prevention, “Essentials of Modeling and Analytics:
Retail Risk Management and Asset Protection.”
David Speights, Ph.D., Chief Data Scientist
Dr. David Speights is the Chief Data Scientist at Appriss
and leads the Data Sciences Team, responsible for applying
advanced analytical techniques, such as artificial intelligence
and machine learning throughout the enterprise. David has more
than 20 years of experience developing and deploying analytical
solutions for more than 100 organizations in multiple industries.
David holds a Ph.D. in Biostatistics from the University of
California, Los Angeles, several patents, and is lead author on
the first book written for analytics applied to retail loss
prevention, “Essentials of Modeling and Analytics: Retail Risk
Management and Asset Protection.”