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 zero-tolerance policing
The 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 disorders
A lack of resources for identifying at risk populations for first responders and jails
A failure of treatment diversion for at risk populations
Strict post-release supervision
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 other factors.
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.
Treatment 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 community.
Treatment should be long enough to address and treat substance abuse and mental health issues.
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.”