Does Criminal History Predict Pre-Incarceration Substance Use and Substance Use Treatment Need Among People in Prison?

Abstract

This paper investigated the degree to which legally-involved individuals’ criminal history was related to their responses on a validated screener for substance use. Using data from 56,000 people released from the Illinois Department of Corrections, this study examined whether prior arrests, arrests for violent crimes, and arrests for drug-law crimes were associated with substance use prior to prison, the identification of which drug caused the individual the most serious problems, and need for substance use treatment. Most of the relationships between criminal history indicators and self-reported substance use were weak. The exception to this trend was a moderate-to-strong correlation between methamphetamine arrests and methamphetamine use, which may reflect how rural jurisdictions in Illinois are using prison as a response to crime. This paper highlights the importance of drug screening to identify drug use problems and treatment need rather than relying solely on criminal history and drug law violations.

Keywords

substance use criminal justice criminogenic needs treatment risk

Introduction

At the end of 2021, 5.4 million people were under the supervision of the United States legal system (Carson & Kluckow, 2023). This includes approximately 1.5 million people—a national increase for the first time since 2005—in jail or prison (Sawyer & Wagner, 2024). Three hundred and sixty thousand people are incarcerated in the United States for a drug-related offense (e.g., possession, distribution; Sawyer & Wagner, 2024), and drug-related crimes account for about a quarter (26%) of all annual arrests (National Center for Drug Abuse Statistics, 2024). Substance use-related difficulties have been documented as a noteworthy behavioral health concern for incarcerated populations (see Staton-Tindall et al., 2011). The consequences of substance use in incarcerated samples includes, but is not limited to, higher rates of recidivism (Zgoba et al., 2020), a greater chance of drug overdose (Binswanger et al., 2016), and an increased all-cause mortality rate (Chang et al., 2015). Consequently, justice agencies provide substance use treatment for people in prison to improve both client and public safety outcomes (De Andrade et al., 2018).

Public health initiatives designed to improve the outcomes of incarcerated populations have led to the implementation of practices that identify, prevent, and treat substance use disorders (Belisle & Solano-Patricio, 2022; Chandler et al., 2009; Russell, 2019). Cascade of Care frameworks, in particular, have been used in prisons to ensure people showing a need for substance use treatment receive programming while incarcerated or following release (Ray et al., 2022). Commonly consisting of screening, assessment, treatment referrals, treatment initiation, and retention (Belenko et al., 2018; Mugavero et al., 2013; Williams et al., 2019), Cascade frameworks are intended to maximize service delivery within the target population. The use of Cascade models with legal samples has been related to improved treatment outcomes for both infectious disease and substance use (Grella et al., 2022; Hariri et al., 2020; Socías et al., 2018). Studies closely examining specific stages of the Cascade (e.g., screening) have the potential to inform practices that aid in the identification and treatment of substance use disorders.

Screening is a crucial step in the identification of people with a need for substance use treatment (Belenko, 2006; self-cite; Kubiak et al., 2005). The screening process can include, for example, justice agencies developing organizational policies that require all persons be screened for a substance use disorder upon admission. The results from a screener can then be used to make evidence-based treatment decisions (e.g., residential vs. outpatient; see Long et al., 2019 for an empirical example). The use of evidence-based screeners in legal settings, however, varies widely across the United States (e.g., Bunting et al., 2023; Ray et al., 2022; Scott et al., 2019; Taxman et al., 2007a, 2007b). Failing to properly screen for substance use may lead to a mismatch between a client's treatment needs and program intensity, which has been shown to impede progress in treatment (e.g., Bonta et al., 2000). In this way, identifying reliable indicators predicting someone's need for substance use treatment can maximize the efficiency and accuracy of the screening process.

Static and Dynamic Risk Factors: Predicting Treatment Need

Indicators warranting intervention is commonly conceptualized as either static or dynamic (see Wong et al., 2009, pp. 84–86). Static risk factors, including age, sex, and criminal history, are immutable characteristics that increase someone's risk for recidivism (Taxman & Smith, 2021, p. 101459). Conversely, dynamic risk/need factors are theoretically malleable (e.g., substance use, mental health, negative peer groups; Andrews & Bonta, 2010) processes that can be intervened upon to lessen one's risk for recidivism. Collectively, the impact of static and dynamic factors is frequently assessed with respect to their association with recidivism.

Studies on how static and dynamic risk factors influence recidivism have produced mixed results (e.g., Caudy et al., 2013; Eisenberg et al., 2019; Wiese, 2022). A recent systematic review, for example, reported that dynamic risk factors (e.g., substance use, employment problems, and marital problems) were comparable to static factors (e.g., age, gender, and criminal history) when predicting recidivism for people with community sentences (Yukhnenko et al., 2020). In contrast, a systematic review including people released from custody concluded that static risk factors were superior to dynamic factors when predicting recidivism (Goodley et al., 2022). Despite mixed findings, the literature is conclusive in that both static and dynamic factors are related to general, sexual, and violent recidivism (r_s = 0.02–0.17; Katsiyannis et al., 2018), suggesting that people with more static and dynamic risk factors would benefit from some form of recidivism reduction program. Additional research is needed on how static and dynamic factors interact to predict risk, and by extension treatment need, so that clinical resources are allocated for those with the highest treatment needs.

Criminal history is an especially robust predictor of recidivism (United States Sentencing Commission, 2017) that could serve as an indicator of treatment need. People with a more extensive criminal history, for instance, have been found to report more physical health problems (Mateyoke-Scrivner et al., 2003) and substance use (Pierce et al., 2017). It may seem reasonable then to assume that someone with a drug-related offense would have a greater need for substance use treatment relative to someone without a drug-related offense. However, there remains limited research on the connection between criminal history, substance use patterns, and substance use treatment need.

Studies have noted differences in drug and alcohol use patterns between people with and without violent offenses (e.g., Håkansson & Jesionowska, 2018; Proctor et al., 2017). For example, people in prison for a violent offense (as compared to people with a drug-related offense) reported less illicit and injection drug use and more binge drinking and sedative drug use (Håkansson & Jesionowska, 2018). Another study found a lower incidence of cocaine dependence among people with a violent crime, but no difference in other drug-specific dependence (e.g., alcohol, marijuana, and heroin) when comparing people with more or less than 10 prior arrests (Proctor et al., 2017). These studies highlight important differences in drug use patterns between people with and without a history of violent crime yet fail to capture how these differences are related to treatment need. By further understanding the relationships among criminal history, substance use, and treatment, justice agencies may be better positioned to serve individuals involved with the justice system. If criminal history is related to substance use, and perhaps more importantly, treatment need, agencies can use this information to identify persons in need of treatment with greater acuity. Conversely, the absence of a relationship between criminal history and substance use behavior would further underscore the need for all people in the justice system to be screened using an evidence-based screener and not to conflate certain criminal history as a reliable indicator of treatment need.

Current Study

The current study sought to examine the extent to which criminal history—a static risk factor for recidivism—served as an indicator of substance use in the year prior to incarceration and current substance use treatment need using a sample of individuals released from prison in the United States. Within the Illinois Department of Corrections, a validated screener for substance use disorders (i.e., Texas Christian University Drug Screen II [TCUDS-II]) assists in the identification of individuals that may need treatment. Without such a screening instrument, identification of who may need treatment would be based solely on current conviction offense or an individual's criminal history. Therefore, this study gauged the degree to which there was a correlation between the extent and nature of a person's criminal history and self-reported substance use behaviors. Overall, the current paper was guided by the following three research questions:

To what degree does a more extensive criminal history correspond to more problematic drug use as reported on the TCUDS-II?

What is the relationship between the number of prior arrests for drug-law violations and self-reported drug use; and does this relationship generalize when examining drug-specific use and offenses?

Does the magnitude of the relationship between criminal history and self-reported drug use vary by assigned sex and race?

Method

Procedure and Sample

The sample used for the analyses included all individuals released from prison in Illinois from January 2018 through December 2022, excluding those who had been admitted to prison for a technical violation of their mandatory supervised release (i.e., “parole”). During that sampling period, there were a total of 68,643 unique individuals released from prison. All people who are admitted to prison in the state of Illinois are required to be administered the TCUDS-II to screen for substance use problems and inform treatment recommendations. This information was used to quantify participants’ self-reported substance use, the substance causing them the most issues in the past 12 months, and treatment need. Using the available inmate ID numbers, we requested participants’ criminal history information through the state's Criminal History Record Information (CHRI). Inconsistencies in reporting resulted in 12,030 people having incomplete records. Only individuals with complete data were included in this study, resulting in a final sample of 56,613 people who had a TCUDS-II score and a complete criminal history record.

Measures

Demographic Information

It is standard practice for prisons in the state of Illinois to collect the demographic information of new residents at intake. This information was used in the current study and included participants’ age, sex, and race/ethnicity.

Criminal History

The current study collected detailed information about participants’ criminal history using the Illinois State Police's CHRI system. Criminal history was recorded as the number of arrests prior to incarceration (i.e., count data), categorized-based on whether these offenses were general, violent, or drug-law violations, and grouped into drug-specific offenses (e.g., alcohol, cannabis, methamphetamine) when possible. Analyses involving offense type relied on the charge listed as the primary charge given individuals could have been sentenced for multiple charges.

Substance Use and Substance Use Treatment Need

Drug use in the year prior to prison, including the substance causing respondents the largest problem, and current treatment need were measured using the TCUDS-II (Institute of Behavioral Research, 2018). The TCUDS-II is a validated screener for substance use that was developed based on the DSM-IV diagnostic criteria for substance use dependence. This screening assessment includes 15 items asking respondents to report on their substance use behavior in the 12 months prior to incarceration. The nine items used to quantify treatment need are presented on a dichotomous scale (No = 0, Yes = 1) Supplementary items that are not included in the scoring of treatment need are included to provide additional information that may inform programing. Of particular interest, participants were asked to indicate the drug that caused them the most serious problems in the past 12 months. Total scores on the TCUDS-II were scored by taking the sum of all items, ranging on a continuous scale from 0 to 9. Scores greater than 3 were used as an indicator of need for a treatment referral and/or treatment services. Using this cut off, we created a dichotomous variable representing treatment (Treatment Need vs. No Treatment Need).

Analytic Plan

Bivariate analyses (e.g., chi-square tests) were performed to examine the relationship between various measures of criminal history (i.e., prior arrests for various offenses) and self-reported drug use from the TCUDS-II. The analyses progressed from examining the relationship between general measures of criminal history—total arrests and total arrests for violent crimes—to measures that were more specific to possession or distribution of drugs.

Similar analyses were performed to determine the relationship between prior arrests on drug charges and whether each specific drug was reported on the TCUDS-II as the drug causing the individual the most serious problems. Due to how Illinois’ drug laws are categorized, it was only possible to isolate arrests involving a few specific substances. For example, prior arrests for offenses that involved alcohol included driving under the influence (DUI), minor in possession, or a violation of Illinois’ Liquor Control Act. It was also possible to isolate prior arrests specifically for methamphetamine offenses (e.g., possession, manufacture, delivery as defined in Illinois’ Methamphetamine Control and Community Protection Act) and prior arrests for offenses involving cannabis (as defined in the Illinois Cannabis Control Act). It is important to note that Illinois legalized recreational cannabis in 2020, and thus arrests for cannabis possession were less likely after that law went into effect. Further, criminal records involving cannabis possession were expunged if they involved 30 grams or less and likely effected the data used in these analyses. Still, one in five of the cases included in the analyses had a prior arrest for a cannabis offense. Most other substances (e.g., cocaine, crack cocaine, and opiates/heroin), however, are all combined into one broad category—the Illinois Controlled Substances Act, and therefore it was not possible to isolate prior arrests involving heroin, for example. Only when relatively large amounts of drugs were involved (e.g., possession) for these Illinois Controlled Substance Act offenses was it possible to distinguish the drug type.

In addition to examining the relationship between self-reported drug use and prior arrests for drug-law violations, we also examined the association between an individual's TCUDS-II score and the number of prior arrests for all crimes and violent crimes. Because of the large sample size (∼56,000 cases), almost all chi-square tests were statistically significant. To measure the strength of the relationship; Kendall's tau-c, Phi, or Spearman's Rho was calculated for each set of bivariate analyses; depending on which was most appropriate given the data structure.

Results

Sample Characteristics

Table 1 summarizes the demographic, current offense and criminal history, and TCUDS-II results for the entire sample. Most individuals in this study were males, Black, and sentenced to prison for a non-violent crime. Over 40% reported at least monthly use of alcohol and cannabis prior to incarceration, while roughly 20% reported monthly use of cocaine, opiates and methamphetamine. The screening portion of the TCUDS-II includes nine questions designed to identify different clusters of substance use symptoms (e.g., physical dependence, substance use interfering with responsibilities, etc.). A score of three or higher on this screener suggests the need for substance use treatment or at least a more thorough diagnostic assessment. Overall, 55% of the sample was considered in need of a treatment referral and/or substance use programing.

Table 1.

Characteristics of Sample of 2018–2022 Illinois Prison Exits (N = 56,613).

	N	%
Sex
Male	51,305	90.6
Female	5,308	9.4
Race
Black	30,014	53.0
White	19,968	35.3
Hispanic	6,082	10.7
Other	549	1.0
Age at prison admission
Under 30	24,942	44.1
30 and older	31,671	55.9
Current offense
Violent	25,559	45.1
Property	13,515	23.9
Drug-law violation	16,523	29.2
Other	1,016	1.8
Past month drug use prior to prison (based on TCUDS)
Alcohol use	27,140	47.9
Cannabis use	24,441	43.2
Cocaine use	12,307	21.7
Opiate use	11,687	20.6
Methamphetamine use	11,776	20.8
TCUDS score of 3 or higher	31,484	55.6
Substance causing most serious problems
Methamphetamine	7,091	12.5
Cannabis	5,446	9.6
Alcohol	8,084	14.3
Prior arrests
1–4	11,606	20.5
5–9	15,340	27.1
10–15	13,003	23.0
16 +	16,664	29.4
Prior arrests for violent crimes
None	8,679	15.3
1–2	19,567	34.6
3+	28,367	50.1
Prior arrests for drug-law violations
None	16,163	28.5
1–2	18,193	32.1
3–6	15,328	27.1
7 or more	6,929	12.2
Has prior arrest for methamphetamine offense	5,561	9.8
Has prior arrest for cannabis offense	11,765	20.8
Has prior arrest for driving under the influence of alcohol offense	14,425	25.5

The average number of total prior arrests was 12.9, with 29% having 16 or more prior arrests. Most (85%) had prior arrests for violent crimes, with roughly 50% having three or more prior arrests for crimes of violence. Additionally, almost 70% had a prior arrest for a drug-law violation. When arrests involving specific drugs were examined, relatively few (9.8%) had a prior arrest for methamphetamine, while 20% had a prior arrest involving cannabis, and 25% had at least 1 prior arrest for DUI of alcohol.

Criminal History, Substance Use, and Treatment Need

When analyses were performed to measure the correlation between general measures of prior arrests (total prior arrests and prior arrests specifically for violent crimes) and whether the individual showed a substance use treatment need, the strength of the relationships were extremely weak (τ_c = 0.02, p < .001). Similar patterns of low correlation were seen when the interval-level measures of prior arrests and ratio-level TCU scores were compared (r_ss ≤ 0.04).

Similarly, low levels of correlation were seen when the number of total prior arrests and prior arrests for violent crimes were compared with whether the individual reported past-month use of specific substances, and these patterns remained when using interval/ratio level measures of prior arrests (r_ss ≤ 0.08). Interestingly, there was a slight inverse correlation between total prior arrests and use of cannabis and methamphetamine (rs ≤ −0.08); as well as a slight inverse correlation between prior arrests for a violent crime and past month use of cannabis, heroin/opiates and methamphetamine (rs ≤ −0.08).

Table 2.

Correlations (Kendal's Tau-c & Phi) Between Criminal History Measures, TCUDS, and Specific Problem Drug.

	TCUDS	Alcohol	Cannabis	Heroin/opiate	Meth	Cocaine
Prior arrests	.02* {.05*}	.01*	−.08* (−.11) [−.16]	.06* (.11*)	−.05* (.10*)	.06* (.13*)
Prior arrests (violent)	−.02* (−.07*)	.06* (.09) {.11}	−.01* [−.06*]	−.02* (−.05*)	−.07* (−.16*)	.05*
Prior arrests (drug-related)	.11* (.16) {.18}	−.00 (−.11) {.06}	−.02* (−.06) [−.08]	.12* (.19*)	.06* (.15) [−.02] {.21*}	.05* (.08*)
prior Sentence (drug-related)	.13*	.01* (−.04*)	−.00	.07*	.11* (.19) {.16}	.02* (−.05*)

Note. *Correlations are statistically significant at an alpha of .05 (females); [black]; {white}. For ease of interpretation, correlation coefficients for subsamples were only reported if larger than that using the full sample.

Table 3.

Correlations (Phi) Between Prior Drug Arrests, Monthly Use and Treatment Need (TCUDS Scores of 3 or Higher).

	Methamphetamine	Cannabis	DUI
Monthly use of that drug	.44	.06	.09
Drug most serious drug problem	.49	.03	.20
Need treatment	.15	−.01	.04

Note. All correlations are all statistically significant at an alpha of .05.

Drug-Related Offenses, Drug-Specific Offenses, and Drug Screening Scores

When the relationship between prior arrests for drug-law violations and measures of treatment need and drug use was examined, similarly low-levels of correlation were found. For example, the highest correlations were seen between prior arrests for drug offenses and treatment need (τ_c = 0.11, p < .001), and prior drug arrests and heroin/opiate use (τ_c = 0.12, p < .001). Finally, the relationship between the offense that resulted in the sentence to prison (i.e., a drug-law violation or some other offense) and treatment need was weak. For example, the strongest correlation was only 0.13 (i.e., r_φ = 0.13, p < .001) between whether the current prison sentence was for a drug offense and the TCUDS-II score.

The structure of Illinois’ drug laws did not allow for the determination of all specific drugs involved in prior arrests. The exceptions are cannabis and methamphetamine—both of which have separate legal classifications allowing for the identification of arrests specifically for offenses involving these substances (e.g., possession, delivery, and manufacture). In addition, the one alcohol offense which can be easily identified in state law and arrest records is DUI of alcohol. Thus, for these three substances—cannabis, methamphetamine, and alcohol—it was possible to examine the correlation between prior arrests for offenses involving these drugs and reported monthly use using the TCUDS-II, whether that drug was identified by the individual as causing them the most problems, and if they showed a need for substance use treatment.

As shown in Table 3, the results of these analyses revealed stronger correlations specifically for methamphetamine arrests, monthly use, and problems. Specifically, the correlation between whether the individual had at least 1 prior arrest involving methamphetamine and if they reported using methamphetamine monthly prior to prison was 0.44, p < .001. Similarly, having a methamphetamine arrest was also strongly correlated (0.49, p < .001) with the individual identifying methamphetamine as causing them the most problems. However, despite these strong correlations, prior arrests for methamphetamine offenses were weakly related to TCUDS-II scores greater than 3 (i.e., treatment need; 0.15, p < .001). Further, it is important to note that those with an arrest for a methamphetamine offense accounted for less than 10% of the total sample. The correlations between cannabis arrests, monthly cannabis use, cannabis causing serious problems, or need for treatment were all weak (0.06 or lower), but could be due to the issue described above regarding the expungement of cannabis possession offenses following the legalization of recreational cannabis in Illinois. Similar weak correlations were seen between arrests for DUI of alcohol and alcohol use and treatment need; a slightly higher correlation (0.20, p < .01) was seen between having a prior DUI arrest and alcohol being identified as the drug causing the individual the most problems.

Discussion

Individuals in jail or prison are negatively affected by substance use-related concerns (Baranyi et al., 2019; Butler et al., 2022; Fazel et al., 2017). Having a diagnosis for a substance use disorder, for instance, has been related to higher rates of recidivism (Zgoba et al., 2020) and drug overdose deaths following release (Binswanger et al., 2016). In this way, there remains a need for jails and prisons to provide substance use programing. The purpose of this paper was to investigate whether criminal history—a static risk factor associated with recidivism outcomes (United States Sentencing Commission, 2017)—was correlated with substance use in the year prior to incarceration and substance use treatment need. This included examining various offense categories, such as general, violent, drug-related, and if people with drug-specific offenses (e.g., methamphetamine, cannabis) were more likely to report substance use and have a substance use treatment need.

The relationship between criminal history—when assessed as the number of total prior arrests and prior violent arrests—and substance use during the past month was weak. These findings contribute to a nascent literature highlighting differences in drug and alcohol use patterns among people with varying criminal histories (Håkansson & Jesionowska, 2018; Proctor et al., 2017). Although differences have been noted, this study found the correlation between criminal history and offense type (general vs. violent) with substance use patterns to be modest in a large representative sample of people in prison. A similar effect was observed when assessing the relationship between criminal history and substance use treatment need. Overall, these analyses suggest that, despite the purported differences in drug and alcohol use patterns, these differences may lack practical significance—indicating that this information should have minimal impact on case management decisions and program assignment.

When examining drug-related charges, the relationships among criminal history, substance use, and treatment need were similarly weak. This pattern of results remained consistent when examining the number of previous drug-related offenses as well as sentencing offenses that involved drug violations. The absence of a notable association among these variables underscores the importance of using evidence-based screeners for substance use rather than a client's history of drug-related violations, especially considering national reports documenting the inconsistent use of screeners in legal settings (e.g., Bunting et al., 2023) Failing to screen people at intake may hinder the identification of treatment needs, prevent service delivery to those in need of care, and cause a mismatch between treatment need and program intensity.

The largest relationship observed in the current study was that between methamphetamine-related violations and self-report methamphetamine use before prison, and between methamphetamine-related violations and methamphetamine being endorsed as the substance causing participants the most difficulties. The strength of the association between methamphetamine offenses, methamphetamine use, and problems with methamphetamine could be a byproduct of how rural communities tend to provide harsher sentences for drug-related crimes when compared to urban communities (e.g., Ruhland & Holmes, 2023). Indeed, 40% of all those in the current study sentenced to prison from rural counties were for drug-law violations, compared to 26% of those sentenced from urban counties. This interpretation of the data suggests that these relationships may be overestimated and is further supported by the strength, or lack thereof, of the associations among arrests, monthly use, and substance use problems for alcohol and cannabis—two substances that are generally considered less “serious” when compared to methamphetamine.

This study can be used to inform how screening practices are used to identify people who could benefit from substance use treatment. An overarching theme of this paper was to evaluate the interaction between a static risk factor for recidivism (i.e., criminal history) and a dynamic treatment need (i.e., substance use). Across analyses, the association between these criminal history and drug-related outcomes was small—suggesting that criminal history should not be used as an indicator of substance use or substance use treatment need. Said differently, having arrests for drug-law violations does not necessarily imply a need for substance use treatment and the absence of arrests for drug-law violations does not indicate the absence of a treatment need. Failing to treat these risk/need factors as independent sources of information may misrepresent someone's risk and needs. Thus, despite the differing use of evidence-based substance use screeners in legal settings (Bunting et al., 2023; Ray et al., 2022; Scott et al., 2019; Taxman et al., 2007a, 2007b), screeners specifically for substance use should be part of the recommended approach for identifying candidates for substance use treatment.

Strengths, Limitations, and Future Directions

There are strengths and limitations of this paper that should be considered when interpreting its’ results. This study used a sample of more than 56,000 people in prison to evaluate the relationship between criminal history, self-reported substance use, and substance use treatment need. This relatively large sample size is an analytic strength in that the magnitude of the associations reported are likely to generalize to that of the population of interest. This analysis, however, involved data collected at a single timepoint and therefore does not represent causal linkages. A more advanced analytic procedure may be useful for simultaneously modeling the relationships among criminal history, substance use, and treatment need, potentially lending itself to a more accurate representation of how these variables are related. With this research, researchers should consider the effect of additional grouping variables not assessed in this investigation. For example, future research may consider using a multilevel framework to answer questions related to how community- (e.g., socioeconomic status) and state-level (e.g., state-specific drug laws) characteristics impact on the associations among criminal history, substance use, and treatment need.

Substance use behavior was collected using the TCUDS-II—a validated screening tool for substance use behavior. This approach could be viewed as a limitation in that it introduces a potential for response bias. Individuals completing the TCUDS-II could have felt pressured to underreport their substance use behavior given this screener was administered while in a custodial setting. Additional research may consider using behavioral (e.g., Timeline Follow Back) or biological (e.g., urinalysis) measures to determine the extent to which these approaches compare to that of self-reported substance use. Another strength of this study was the inclusion of drug-specific charges when comparing the relationship between criminal history and substance-specific use prior to incarceration. This allowed for the evaluation of the relationship between drug-specific offenses (e.g., prior arrests involving methamphetamine) and substance use treatment need for a particular substance. Alternatively, this could also be seen as a shortcoming in that information related to drug-specific offenses were limited to cannabis, methamphetamine, and alcohol. Analyses involving offense category were limited in that we relied exclusively on participating primary sentencing offense. This ignores the fact that individuals could have been sentenced for multiple charges. Participants’ charges were categorized into general, violent, and drug-law violations for ease of interpretation and based on previous work examining criminal history as an independent variable (e.g., Håkansson & Jesionowska, 2018; Proctor et al., 2017). However, this approach oversimplifies criminal history to some extent in that it does not account for their underlying motivations, such as drug use being a motivating factor for some other crime.

Conclusions

In conclusion, people involved with the legal system are adversely affected by substance use, and it is therefore imperative for the legal system to provide evidence-based programs that address substance use treatment needs. These efforts are dependent upon effective screening procedures that accurately pinpoint people in jail or prison in need of substance use treatment. The present study found that criminal history—a static risk factor associated with recidivism—was weakly associated with substance use behavior and need for substance use treatment. The exception to this trend was the moderate association between drug-specific offenses for methamphetamine. The results of this study support screening and assessment protocols that treat criminal history and substance use severity as independent risk/need factors that can be used to inform case management and assignment to substance use treatment.

Footnotes

Author Note

The authors of this paper declare no conflict of interest. This research was supported by the National Institute on Drug Abuse under grant UG1 DA050074. The de-identified data associated with this project is available upon request. Correspondence concerning this article should be addressed to Thomas B. Sease, 2800 S University Drive, Fort Worth, TX 76129, USA. E-mail: thomas.b.sease@tcu.edu

ORCID iD

Thomas B. Sease

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute on Drug Abuse (grant number UG1 DA050074).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Author Biographies

Thomas Sease is an Associate Research Scientist at Texas Christian University's Institute of Behavioral Research. Thomas graduated in 2023 with his PhD in Experimental Psychology and a minor in Statistics. His research interests include screening and assessment, substance use and mental health, and infectious disease in justice populations.

Renee Castillo is a PhD student at Texas Christian University's Institute of Behavioral Research. She graduated in 2022 with a BS in Psychology and a minor in Child Development. Her research interests include prosocial behavior, substance use, and social provision for people post-incarceration.

Kevin Knight is Director of Texas Christian University's Institute of Behavioral Research and holds the Saul B. Sells Endowed Chair of Psychology. He has dedicated his career to researching substance use, HIV/AIDS, and individuals involved in the criminal justice system. He has served as PI on multiple NIH grants, including the TCU Hub of the Justice Community Opioid Innovation Network, a multi-site project that focuses on supporting individuals with substance use disorders transitioning from prison back into the community, and the Criminal Justice Drug Abuse Treatment Studies, a multi-site research initiative aimed at improving the implementation of evidence-based practices in criminal justice settings.

David Olson is a Professor in the Criminal Justice and Criminology Department at Loyola University Chicago and Co-Director of Loyola's interdisciplinary Center for Criminal Justice. During his 35 years in the field of criminal justice, David has worked with a variety of federal, state, and local agencies to develop and evaluate programs and policies, particularly in community and institutional corrections. Dr. Olson serves as chair of the advisory boards of the Illinois Department of Corrections, the Illinois Department of Juvenile Justice, and the Illinois Criminal Justice Information Authority through separate gubernatorial appointments. Dr. Olson received his B.S. in Criminal Justice from Loyola, his M.A. in Criminal Justice from the University of Illinois at Chicago (UIC), and his Ph.D. in Political Science/Public Policy Analysis from UIC, where he was the recipient of the Assistant United States Attorney General's Graduate Research Fellowship.

Jennifer Becan, Ph.D. has served as Principal Investigator and Co-Investigator (Co-I) on NIDA-funded research projects investigating how implementation strategies at the system, organization, and client level synergistically relate to adoption and sustainability of substance use treatment services within criminal legal and community-based behavioral health service organizations; as well as development and testing of motivational enhancement interventions to promote health risk reduction among criminal legal populations. Dr. Becan graduated with a Master's in Industrial Organizational Psychology from Stephen F. Austin State University in 2005 and a Ph.D. from the Texas Christian University (TCU) Experimental Psychology Program in 2010. She currently serves as a Senior Research Scientist at the TCU Institute of Behavioral Research in Fort Worth, Texas.

Martyna Malecka is a graduate research assistant at Loyola University Chicago's Center for Criminal Justice pursuing their MA in Criminal Justice and Criminology.

Patrick Carabell is a graduate research assistant at Loyola University Chicago's Center for Criminal Justice pursuing their MA in Criminal Justice and Criminology.

References

Andrews

D. A.

Bonta

(2010). The psychology of criminal conduct (5th ed.). Anderson Publishing Co.

Baranyi

Scholl

Fazel

Patel

Priebe

Mundt

A. P.

(2019). Severe mental illness and substance use disorders in prisoners in low-income and middle-income countries: A systematic review and meta-analysis of prevalence studies. The Lancet Global Health, 7(4), e461–e471. https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(18)30539-4/fulltext

Belenko

(2006). Assessing released inmates for substance-abuse-related service needs. Crime & Delinquency, 52(1), 94–113. https://doi.org/10.1177/0011128705281755

Belenko

Johnson

I. D.

Taxman

F. S.

Rieckmann

(2018). Probation staff attitudes toward substance abuse treatment and evidence-based practices. International Journal of Offender Therapy and Comparative Criminology, 62(2), 313–333. https://doi.org/10.1177/0306624X16650679

Belisle

L. A.

Solano-Patricio

E. D. C.

(2022). Harm reduction: A public health approach to prison drug use. International Journal of Prisoner Health, 18(4), 458–472. https://doi.org/10.1108/IJPH-06-2021-0053

Binswanger

I. A.

Stern

M. F.

Yamashita

T. E.

Mueller

S. R.

Baggett

T. P.

Blatchford

P. J.

(2016). Clinical risk factors for death after release from prison in Washington State: A nested case–control study. Addiction, 111(3), 499–510. https://doi.org/10.1111/add.13200

Bonta

Wallace-Capretta

Rooney

(2000). A quasi-experimental evaluation of an intensive rehabilitation supervision program. Criminal Justice and Behavior, 27(3), 312–329. https://doi.org/10.1177/0093854800027003003

Bunting

A. M.

Nowotny

Farabee

McNeely

Beckwith

C. G.

(2023). Characteristics of substance use screening at intake in a sample of US jails. Journal of Health Care for the Poor and Underserved, 34(1), 180–191. https://doi.org/10.1353/hpu.2023.0012

Butler

Nicholls

Samji

Fabian

Lavergne

M. R.

(2022). Prevalence of mental health needs, substance use, and co-occurring disorders among people admitted to prison. Psychiatric Services, 73(7), 737–744. https://doi.org/10.1176/appi.ps.202000927

10.

Carson

Kluckow

(2023). Correctional populations in the United Sates, 2021 – Statistical tables. https://bjs.ojp.gov/document/cpus21st.pdf

11.

Caudy

M. S.

Durso

J. M.

Taxman

F. S.

(2013). How well do dynamic needs predict recidivism? Implications for risk assessment and risk reduction. Journal of Criminal Justice, 41(6), 458–466. https://doi.org/10.1016/j.jcrimjus.2013.08.004

12.

Chandler

R. K.

Fletcher

B. W.

Volkow

N. D.

(2009). Treating drug abuse and addiction in the criminal justice system: Improving public health and safety. JAMA, 301(2), 183–190. https://doi.org/10.1001/jama.2008.976

13.

Chang

Lichtenstein

Larsson

Fazel

(2015). Substance use disorders, psychiatric disorders, and mortality after release from prison: A nationwide longitudinal cohort study. The Lancet Psychiatry, 2(5), 422–430. https://doi.org/10.1016/S2215-0366(15)00088-7

14.

De Andrade

Ritchie

Rowlands

Mann

Hides

(2018). Substance use and recidivism outcomes for prison-based drug and alcohol interventions. Epidemiologic Reviews, 40(1), 121–133. https://doi.org/10.1093/epirev/mxy004

15.

Eisenberg

M. J.

Van Horn

J. E.

Dekker

J. M.

Assink

Van Der Put

C. E.

Hendriks

Stams

G. J. J.

(2019). Static and dynamic predictors of general and violent criminal offense recidivism in the forensic outpatient population: A meta-analysis. Criminal Justice and Behavior, 46(5), 732–750. https://doi.org/10.1177/0093854819826109

16.

Fazel

Yoon

I. A.

Hayes

A. J.

(2017). Substance use disorders in prisoners: An updated systematic review and meta-regression analysis in recently incarcerated men and women. Addiction, 112(10), 1725–1739. https://doi.org/10.1111/add.13877

17.

Goodley

Pearson

Morris

(2022). Predictors of recidivism following release from custody: A meta-analysis. Psychology, Crime & Law, 28(7), 703–729. https://doi.org/10.1080/1068316X.2021.1962866

18.

Grella

C. E.

Ostlie

Watson

D. P.

Scott

C. K.

Carnevale

Dennis

M. L.

(2022). Scoping review of interventions to link individuals to substance use services at discharge from jail. Journal of Substance Abuse Treatment, 138, Article 108718. https://doi.org/10.1016/j.jsat.2021.108718

19.

Håkansson

Jesionowska

(2018). Associations between substance use and type of crime in prisoners with substance use problems – a focus on violence and fatal violence. Substance Abuse and Rehabilitation, 9, 1–9. https://doi.org/10.2147/SAR.S143251

20.

Hariri

Sharafi

Sheikh

Merat

Hashemi

Azimian

Tamadoni

Ramazani

Mehdi Gouya

Abbasi

Tashakorian

Alasvand

Alavian

S. M.

Malekzadeh

(2020). Continuum of hepatitis C care cascade in prison and following release in the direct-acting antivirals era. Harm Reduction Journal, 17, 1–9. https://doi.org/10.1186/s12954-020-00431-x

21.

Katsiyannis

Whitford

D. K.

Zhang

Gage

N. A.

(2018). Adult recidivism in United States: A meta-analysis 1994–2015. Journal of Child and Family Studies, 27, 686–696. https://doi.org/10.1007/s10826-017-0945-8

22.

Kubiak

S. P.

Boyd

C. J.

Slayden

Young

(2005). The substance abuse treatment needs of prisoners: Implementation of an integrated statewide approach. Journal of Offender Rehabilitation, 41(2), 1–19. https://doi.org/10.1300/J076v41n02_01

23.

Long

J. S.

Sullivan

Wooldredge

Pompoco

Lugo

(2019). Matching needs to services: Prison treatment program allocations. Criminal Justice and Behavior, 46(5), 674–696. https://doi.org/10.1177/0093854818807952

24.

Mateyoke-Scrivner

Webster

J. M.

Hiller

M. L.

Staton

Leukefeld

(2003). Criminal history, physical and mental health, substance abuse, and services use among incarcerated substance abusers. Journal of Contemporary Criminal Justice, 19(1), 82–97. https://doi.org/10.1177/1043986202239743

25.

Mugavero

M. J.

Amico

K. R.

Horn

Thompson

M. A.

(2013). The state of engagement in HIV care in the United States: From cascade to continuum to control. Clinical Infectious Diseases, 57(8), 1164–1171. https://doi.org/10.1093/cid/cit420

26.

National Center for Drug Abuse Statistics. (2024). Drug related crime statistics. https://drugabusestatistics.org/drug-related-crime-statistics/#:∼:text=1.16%20million%20Americans%20are%20arrested%20annually%20for%20the%20sale%2C%20manufacture,all%20arrests%20in%20the%20U.S

27.

Pierce

Hayhurst

Bird

S. M.

Hickman

Seddon

Dunn

Millar

(2017). Insights into the link between drug use and criminality: Lifetime offending of criminally-active opiate users. Drug and Alcohol Dependence, 179, 309–316. https://doi.org/10.1016/j.drugalcdep.2017.07.024

28.

Proctor

S. L.

Alvarez de la Campa

G. J.

Medina-Reyes

Hoffmann

N. G.

(2017). Clinical and demographic correlates of the type and frequency of criminal behavior among jail inmates with a substance use disorder. American Journal of Criminal Justice, 42, 746–758. https://doi.org/10.1007/s12103-016-9381-3

29.

Ray

Victor

Cason

Hamameh

Kubiak

Zettner

Dunnigan

Comartin

Costello

(2022). Developing a cascade of care for opioid use disorder among individuals in jail. Journal of Substance Abuse Treatment, 138, Article 108751. https://doi.org/10.1016/j.jsat.2022.108751

30.

Ruhland

E. L.

Holmes

(2023). An examination of sentencing outcomes in rural and urban locations. American Journal of Criminal Justice, 48(3), 701–722. https://doi.org/10.1007/s12103-022-09678-5

31.

Russell

S. F.

(2019). Second looks at sentences under the First Step Act. Federal Sentencing Reporter, 32(2), 76–85. https://doi.org/10.1525/fsr.2019.32.2.76

32.

Sawyer

Wagner

(2024). Mass incarceration: The whole pie 2024. https://www.prisonpolicy.org/reports/pie2024.html

33.

Scott

C. K.

Dennis

M. L.

Grella

C. E.

Funk

R. R.

Lurigio

A. J.

(2019). Juvenile justice systems of care: Results of a national survey of community supervision agencies and behavioral health providers on services provision and cross-system interactions. Health & Justice, 7(1), 1–18. https://doi.org/10.1186/s40352-019-0093-x

34.

Socías

M. E.

Wood

Kerr

Nolan

Hayashi

Nosova

Montaner

Milloy

M. J.

(2018). Trends in engagement in the cascade of care for opioid use disorder, Vancouver, Canada, 2006–2016. Drug and Alcohol Dependence, 189, 90–95. https://doi.org/10.1016/j.drugalcdep.2018.04.026

35.

Staton-Tindall

Havens

J. R.

Oser

C. B.

Burnett

M. C.

(2011). Substance use prevalence in criminal justice settings. In Leukefeld

Gullotta

T. P.

Gregrich

(Eds.) & J. M. Ramos (Collaborator), Handbook of evidence-based substance abuse treatment in criminal justice settings (pp. 81–101). Springer Science + Business Media. https://doi.org/10.1007/978-1-4419-9470-7_6

36.

Taxman

F. S.

Smith

(2021). Risk-need-responsivity (RNR) classification models: Still evolving. Aggression and Violent Behavior, 59, Article 101459. https://doi.org/10.1016/j.avb.2020.101459

37.

Taxman

F. S.

Cropsey

K. L.

Young

D. W.

Wexler

(2007a). Screening, assessment, and referral practices in adult correctional settings: A national perspective. Criminal Justice and Behavior, 34(9), 1216–1234. https://doi.org/10.1177/0093854807304431

38.

Taxman

F. S.

Young

D. W.

Wiersema

Rhodes

Mitchell

(2007b). The national criminal justice treatment practices survey: Multilevel survey methods and procedures. Journal of Substance Abuse Treatment, 32(3), 225–238. https://doi.org/10.1016/j.jsat.2007.01.002

39.

United States Sentencing Commission. (2017). The past predicts the future: Criminal history and recidivism of federal offenders. Government Printing Office.

40.

Wiese

A. L.

(2022). 25-year outcomes of an in-prison therapeutic community in Texas. Unpublished doctoral dissertation, Texas Christian University.

41.

Williams

A. R.

Nunes

E. V.

Bisaga

Pincus

H. A.

Johnson

K. A.

Campbell

A. N.

Remien

R. H.

Crystal

Friedmann

P. D.

Levin

F. R.

Olfson

(2019). Developing an opioid use disorder treatment cascade: A review of quality measures. Journal of Substance Abuse Treatment, 91, 57–68. https://doi.org/10.1016/j.jsat.2018.06.001

42.

Wong

S. C. P.

Olver

M. E.

Stockdale

K. C.

(2009). The utility of dynamic and static factors in risk assessment, prediction, and treatment. In Andrade

J. T.

(Ed.), Handbook of violence risk assessment and treatment: New approaches for mental health professionals (pp. 83–120). Springer Publishing Company. https://psycnet.apa.org/record/2009-05958-003

43.

Yukhnenko

Blackwood

Fazel

(2020). Risk factors for recidivism in individuals receiving community sentences: A systematic review and meta-analysis. CNS Spectrums, 25(2), 252–263. https://doi.org/10.1017/S1092852919001056

44.

Zgoba

K. M.

Reeves

Tamburello

Debilio

(2020). Criminal recidivism in inmates with mental illness and substance use disorders. Journal of the American Academy of Psychiatry and the Law, 48(2), 209–215. https://doi.org/10.29158/JAAPL.003913-20