Substance Involvement and Probation Outcomes: Evidence From a Cohort Study

Abstract

Substance use disorders are common among justice-involved populations, the majority of whom are under community supervision in the form of probation. Substance involvement can amplify the challenges of complying with requirements of probation supervision, violations of which can lead to incarceration. In this study, we assess the role of substance involvement in violations of probation conditions across 47 counties representing 70% of individuals sentenced to probation in the state of Pennsylvania. We also consider the role of court-ordered treatment. We conclude by estimating the consequences of resentencing for substance-involved individuals in Pennsylvania (in incarceration and supervision days). Results suggest that individuals who are substance-involved are at greater risk of technical violations. However, treatment may reduce some negative outcomes for substance-involved individuals. Court-ordered treatment was associated with a reduction in the likelihood of being resentenced for a new offense relative to individuals who were substance-involved but not ordered to treatment.

Keywords

probation community supervision criminal justice technical violations

Introduction

In 2019, there were approximately 6.3 million individuals under correctional supervision in the United States—of these, over two-thirds were under community supervision in the form of probation and, to a lesser extent, parole (Minton et al., 2020). While the probation population is more demographically akin to the general population than the incarcerated population, men and Black individuals are still over-represented in probation (Bronson & Carson, 2019; Kaeble, 2018; Phelps, 2018). Both research and the media tend to focus on mass incarceration exclusively, yet community supervision populations have paralleled the growth in incarceration in a phenomenon some scholars refer to as “mass probation” (Phelps, 2013). The astounding size of the probation population, with over 4.3 million probationers in 2019 (Minton et al., 2020), reflects not only the growing reach of the correctional system but also the vast increase in exposure to potential incarceration, especially among individuals who would formerly not have been exposed to criminal justice supervision (Phelps, 2020).

While probation is formally a community-based sentence, individuals remain at risk for incarceration due to high levels of surveillance and social control throughout their supervision term. The requirements of probation sanctions often mean that probation clients can be incarcerated absent any criminal behavior through “technical violations” of their supervision. In most jurisdictions, any violation of probation conditions—such as failure to report to the probation officer, or failing to maintain certain living or employment conditions—can result in incarceration, with community supervision-related incarceration accounting for up to 70% of prison admissions in some states (The Pew Charitable Trusts, 2018). Probation revocations for purely technical violations (e.g., failure to report and positive drug test) can result in substantial periods of incarceration—sometimes even longer than 6 months, depending on jurisdiction—according to research by the Robina Institute of Criminal Law and Criminal Justice (Alper & Ruhland, 2016; see also Cohen, 1995; Stickels, 2007) and may be responsible for up to 25% of prison admissions (Council for State Governments, 2019).

Rates of substance use, misuse, and dependence are significantly higher among individuals involved in the criminal justice system than among the general population (Bronson, Stroop, Zimmer, & Berzofsky, 2017). While exact prevalence rates from criminal justice agencies are unknown, data from the National Survey on Drug Use and Health suggest that slightly less than one-third of all individuals under probation supervision have a substance use disorder, compared to the estimated 7% of the general population (Substance Abuse and Mental Health Services Administration, 2020). Older data suggest that alcohol and drug use at the time of the current sanctioned offense are very common among adult probationers, especially those offenses resulting in felonies (Mumola & Bonczar, 1998). High rates of substance involvement among probationers are disconcerting given evidence that substance use, especially substance use without appropriate treatment, may increase the likelihood of poor probation outcomes (Johnson & Jones, 1998; Olson & Lurigio, 2000; Stevens-Martin, Oyewole, & Hipolito, 2014).

With over half of justice-involved individuals screening as having a substance use disorder (Bronson et al., 2017), and similarly high rates among probationers (Substance Abuse and Mental Health Services Administration, 2020), it is critical to assess how substance involvement impacts probation outcomes. Substance involvement creates a unique challenge in community supervision given the challenges accompanying recovery and the frequency of relapse amongst those with substance use disorders (Dennis, Scott, Funk, & Foss, 2005; Hser, Evans, Grella, Ling, & Anglin, 2015; Nosyk, Anglin, Brecht, Lima, & Hser, 2013). While some research suggests that treatment improves probation outcomes for substance-involved probationers (Albonetti & Hepburn, 1997; Evans, Longshore, Prendergast, & Urada, 2006; Holloway, Bennett, & Farrington, 2006; Morash, Kashy, Smith, & Cobbina, 2019), most studies use data from a limited number of probation offices or evaluate specific programs. One meta-analysis found support for treatment when included with intensive supervision (Drake, 2011), though intensive supervision can create challenges for probation success through technical violations (Hyatt & Barnes, 2014; Petersilia & Turner, 1993).

Given the sheer number of individuals on probation, and the consequent potential for incarceration due to technical violations (i.e., net widening), research on community supervision outcomes is vital. However, prior literature is minimal and is often limited to studies of one or a handful of jurisdictions. In this study, we assess probation outcomes across 47 counties in the state of Pennsylvania. Utilizing a sample of 66,675 probationers from urban, suburban, and rural counties across the state, we explore the relationship between substance involvement and both new offenses and technical violations by comparing outcomes among individuals with known substance involvement relative to those who do not. We also consider whether treatment ordered at the time of sentencing reduces the likelihood of negative probation outcomes among those with known substance involvement. Finally, we describe the consequences of resentencing for substance-involved individuals in Pennsylvania.

Correlates of Probation Violation and Revocation

Probation varies significantly across individuals in both the conditions imposed and the intensity of supervision to which the individual is subjected. Common conditions of probation include abiding by all laws, not associating with other justice-involved persons, maintaining employment, attending regular meetings at the probation office, paying fees (e.g., court, supervision, or restitution fees), attending educational or treatment programs, and submitting to regular alcohol and drug testing (Mitchell, Reitz, Watts, & Ellis, 2014). Any violation of these conditions can result in a probation revocation, or termination of probation and imposition of a harsher sentence, often incarceration. Probation violations can be categorized into two broad categories: the commission of a new criminal offense and “technical violations,” that is, violation of the conditions of probation, such as evidence of drug or alcohol use from urinalysis or failing to report for a meeting with their probation officer. Technical violations are a common source of incarceration for individuals on probation. The majority of probationers who are incarcerated during their period of probation are sentenced under their current sentence (as opposed to under a new charge/sentence), indicating either a technical violation or a non-felony offense (Glaze & Bonczar, 2009; Kaeble & Alper, 2020; Kaeble & Bonczar, 2016).

Probation completion rates have always varied, and still vary, from state to state (Hughes, et al., 2001; Kaeble & Alper, 2020). However, rates of violation are climbing. In 1985, approximately 80% of probationers completed their supervision term successfully compared to 58% in 2018 (Bureau of Justice Statistics, 1990; Kaeble & Alper, 2020). Ever-expanding probation conditions and increasing intensity of surveillance have been suggested as possible causes of these declining rates of probation completion (Wodahl, Ogle, & Heck, 2011).

As with other criminal justice outcomes, younger individuals and men—especially Black and Hispanic men—are at greater risk of probation revocation (Ho, Breaux, & Jannetta, 2014; Minor, Wells, & Sims, 2003; Olson & Lurigio, 2000; Steinmetz & Henderson, 2016; Stevens-Martin et al., 2014; Tapia & Harris, 2006) and may also be more likely to experience rearrest (Olson, Alderden, & Lurigio, 2003) and probation failure (Steinmetz & Henderson, 2015). Individuals convicted of more serious crimes (Olson & Lurigio, 2000; Steinmetz & Henderson, 2016) and those with prior criminal justice contact are at higher risk for probation revocation generally (Olson & Lurigio, 2000) and for new crimes (Olson et al., 2003), and are less likely to successfully complete probation (Morgan, 1995). Similarly, probation revocation is more likely among individuals identified as having greater criminogenic risks and needs (Steinmetz & Henderson, 2016), including revocation due to technical violations (Hildebrand, Hol, & Bosker, 2013).

Substance Use and Probation Outcomes

In 1995, over two-thirds of probationers were drug-involved—that is, were convicted of a substance-related offense, using drugs at the time of the offense, or actively using illicit substances in the month prior to the offense (Mumola & Bonczar, 1998). A smaller but more recent sample of individuals on community supervision found that more than 75% were in need of substance use treatment (Owens, Rogers, & Whitesell, 2011). Substance use and misuse by individuals on probation is of critical importance not only because of the relationship between substance use and the commission of crimes to support use (Goldstein, 1985) but also because abstinence is typically a requirement of supervision.

Given the physiological effects of substance use disorders (Volkow, Koob, & McLellan, 2016), it is exceedingly difficult to cease substance use abruptly, which creates important barriers for individuals under probation supervision. Most directly, individuals under supervision are often directly prohibited from consuming substances as a term of probation (often including otherwise legal substances, i.e., alcohol). With varying frequency, individuals are subject to urinalysis; a positive screen is grounds for increasing supervision requirements, or—worse—a technical violation. Additionally, substance use affects an individual’s ability to comply with other requirements of probation, such as attaining and maintaining employment, especially for those who have criminal justice histories (Dunigan et al., 2013; Sigurdsson, Ring, O’Reilly, & Silverman, 2012). Substance use also complicates individuals’ abilities to maintain access to housing (Tsai & Huang, 2019), and even meeting probation officers at specific times and places.

Continued substance use is not only a barrier to successful probation completion because of abstinence and other supervision requirements but misuse of substances also has a strong relationship with continued criminal activity beyond substance use (The Effectiveness of Diversion Programmes for Offenders Using Class A Drugs: A Systematic Review and Meta-Analysis, Hayhurst et al., 2019). Involvement may be especially consequential for women, for whom substance use represents a more substantial pathway to criminal involvement (Hall, Bell, & Carless, 1993; Salisbury & Van Voorhis, 2009; Simpson, Yahner, & Dugan, 2008; Tripodi & Pettus-Davis, 2013). A meta-analysis of drug misuse and crime found that the relationships were strongest for involvement in shoplifting, burglary, and prostitution (Bennett, Holloway, & Farrington, 2008), which may indicate that individuals commit income-generating crimes to facilitate use (Goldstein, 1985).

There is evidence to suggest that substance use treatment reduces the likelihood of poor probation outcomes (Albonetti & Hepburn, 1997; Holloway et al., 2006), especially when targeted at individuals with the greatest risk of continued criminal involvement (Morash et al., 2019). Data from California suggest that agonist treatment for opioid use disorder may reduce the costs of crime by more than $17,000 per individual treated (Krebs et al., 2017). Bondurant et al. (2018) further demonstrate that additional substance use treatment centers reduce county crime costs by $2.9 million. Despite this, the provision of substance use treatment to probationers is relatively rare (Krawczyk et al., 2017; Mumola & Bonczar, 1998; The Pew Charitable Trusts, 2018). Most community corrections departments coordinate at least one substance-related treatment program, but only about 10% of each agency’s probationers and parolees have access to that treatment; further, what programs are available are generally educational and of limited intensity—that is, not evidence-based (Taxman, Perdoni, & Harrison, 2007). While access to medication-assisted treatment options for opioid use disorders (e.g., buprenorphine, naltrexone, or methadone) are increasingly available for justice-involved populations, individual outpatient programs for other substances and inpatient programs broadly remain limited, especially those offering more intense treatment outcomes and in suburban and rural settings (Han, Compton, Blanco, & Colpe, 2017; Krawczyk et al., 2017; Tsai & Gu, 2019). For example, the most commonly reported treatment programs used by probationers in 2009–2013 were self-help in nature (Saloner, Bandara, McGinty, & Barry, 2016). This is concerning given that a large share of people with high substance use disorder severity—and, therefore, a greater need for treatment—are justice-involved (Caulkins et al., 2019).

The Present Study

While significant research suggests that substance use creates challenges for individuals on probation, extant research is generally limited to predicting probation failures for individuals who have substance use issues in small samples. Evaluations of specific treatment programs suggest improved outcomes among those receiving treatment, yet limited work has examined the extent to which treatment moderates the relationship between substance use and probation outcomes in a large sample of substance-involved offenders. In this paper, we assess differences in outcomes between substance-involved probationers and those without known substance involvement in a large sample and the degree to which court-ordered treatment may improve outcomes. Specifically, we address the following research questions:

1) How does the incidence of resentencing vary between probationers who are substance-involved compared to those with no known involvement?

2) How does treatment affect outcomes for substance-involved individuals on probation?

3) What are the total and average consequences of resentencing for probationers who are substance-involved?

Data

Beginning in 2016, the Pennsylvania Commission on Sentencing (PCS) began requiring counties to report the resentencing outcomes for individuals under community supervision. Resentencing is the formal court process of sanctioning a violation of community supervision conditions; it may result in revocation (the termination of community supervision and subsequent incarceration) but may also result in the extension of community supervision. While reporting is mandatory, PCS lacks a mechanism to enforce these requirements. Eleven out of 67 counties reported zero resentencing events between 2016 and 2019; an additional nine counties reported events equivalent to a less than one percent rate of resentencing, suggesting poor data quality. Analyses are thus based on the remaining 47 counties, allowing us to speak to general trends in Pennsylvania. These counties include both of Pennsylvania’s largest cities (Philadelphia and Pittsburgh) as well as many rural and suburban counties.

To allow for a sufficient period of observation, the sample (N = 66,675) for these analyses includes all individuals sentenced between January 1, 2016 and December 31, 2017 for whom probation or a county-based intermediate punishment with restrictive elements (hereafter, CIP) is the most serious sanction across all offenses. CIP sentences are overseen by probation departments. The most frequent forms of CIP in our sample are house arrest and electronic monitoring; typically, these sentences last for 3–6 months after which the offender is transferred to traditional probation supervision. We matched this sentencing cohort to resentencing events as reported to PCS through May 8, 2019 using individual, case, and offense identifiers. Cases for which no match is found in the resentencing data are assumed not to have experienced a resentencing event in the term of their community supervision. The average length of supervision time in our sample is 16 months, meaning that data on most offenders is available for the entirety of their supervision. Offenders for whom supervision was still ongoing at the time of record collection generally had sentences in excess of 2 years and had been followed for a period of at least 16.5 months (median 2 years).

Substance Involvement and Treatment

We identified individuals as substance-involved if they met at least one of the following conditions: (a) sentenced for a drug- or alcohol-related offense¹ (whether it was the most serious charge or not); (b) assessed as substance dependent prior to sentencing; and (c) sentenced to substance use treatment as a condition of probation or county intermediate punishment (CIP).² This operationalization is consistent with previous efforts to identify substance-involvement in criminal justice samples (Mumola & Bonczar, 1998), given that formal assessments for substance use and abuse are rare prior to sentencing and are not uniformly conducted even at probation intake. This almost certainly produces an undercount of substance-involved offenders as we are not able to identify individuals who were under the influence of substances at the time of their offending or who committed instrumental offenses to fund substance use. As a result, our identification strategy will produce conservative estimates of the differences between substance-involved and non-substance-involved individuals under community supervision (i.e., our coefficients are biased toward findings of no difference).

Our determination of substance use treatment is based on data provided at the time of sentencing and is most clearly defined as the condition of being ordered to participate in substance use treatment as a condition of probation. This variable does not, however, reflect actual participation in treatment. There are two important ways in which this might affect our findings. First, some individuals who are ordered to treatment might ultimately not engage with a treatment provider. This will have the effect of biasing our estimates of treatment toward zero—as in, those receiving treatment are not statistically different in their outcomes relative to those not receiving treatment; consequently, any significant findings are conservative estimates. Alternatively, some individuals who are not ordered to participate in treatment at the time of sentencing might choose independently or in coordination with their probation officer to begin some sort of treatment program. This too, however, would bias our coefficients toward zero. As a result, any significant findings related to treatment should be considered as conservative impacts.

Analyses

We first explore the likelihood of resentencing for technical violations and resentencing for new offenses. Because new offenses and technical violations are not mutually exclusive outcomes, we consider them as distinct (i.e., individuals who are resentenced for both technical violations and new offenses are coded “1” in each model). Logit models include controls for sex (male, female [reference]), race (Black, White [reference]) the most serious offense at conviction (property, drug, driving under the influence [DUI], violent [reference], other), total length of supervision (in months), type of supervision (CIP, traditional probation [reference]), grade of offense (felony, misdemeanor [reference]), and the individual’s prior record score (0 [reference], 1–2, 3–4, 5+). We also incorporate county fixed effects in all models to account for jurisdictional variation in practices and reporting. To determine the relationship of substance use treatment with probation outcomes, we limit our sample to only those probation clients who can be identified as substance-involved. Of particular interest in these models is the variable indicating whether or not the individual was ordered to receive substance use treatment services at the time of sentencing.

Finally, we also describe the experienced consequences of resentencing for individuals in our sample, focusing on (a) the most serious type of sanction imposed at resentencing (b) the imposition of additional probation time, and (c) the length of additional criminal justice contact. Our analyses for these outcomes are largely descriptive, providing an illustration of the costs of community supervision violations for substance-involved probationers, though we provide and discuss multivariate analyses in the Appendix (A4).

Results

Sample Descriptives

Our sample is predominantly comprised of offenders in the least serious sentencing matrix cells (see Appendix A1). Most offenders (55%) were in Level 2, where the recommended sentence allows for non-confinement sanctions, including probation and intermediate punishment. More than half of our sample had the lowest possible prior record score. Approximately 13% of our sample experienced at least one resentencing event (Table 1). Twelve percent of the sample was resentenced for at least one technical violation of supervision (e.g., failed drug test, failure to report) while only 3% of the sample was resentenced for a new offense.³ According to our identification criteria, 52% of all offenders sentenced to community supervision in Pennsylvania are substance-involved; of these, 15.7% were ordered to treatment at the time of sentencing. Most of the individuals in our sample are have only minor experience with the criminal justice system; the modal category of prior record scores was zero, and the most common offense was a misdemeanor drug charge. Nearly 20% of the sample was sentenced to a term of CIP that included restrictive elements (e.g., electronic monitoring and house arrest). A comparison of substance-involved individuals under community supervision and those without known substance involvement demonstrate few statistically significant differences (see Appendix A2). However, despite similar prior records and offense categories, substance-involved persons are more likely to experience CIP.

Table 1.

Sample Descriptive Statistics.

Variable		N	Percent	Average
Total		66,675	—	—
Resentenced	Any	8990	13.5%	—
	Technical violation	8030	12.0%	—
	New offense	1994	3.0%	—
Sex	Male	47,295	70.9%	—
Sex	Female	19,380	29.1%	—
Race	White	48,483	72.7%	—
	Black	16,895	25.3%	—
	Hispanic	191	.3%	—
	Other	296	.4%	—
Known substance involvement		34,688	52.0%	—
	Treatment ordered	5449	15.7%	—
Age				34.76
Offense type	Violent	10,193	15.3%
	Property	15,256	22.9%
	Drugs	18,399	27.6%
	DUI	13,657	20.5%
	Other type	9170	13.8%
Grade	Felony	10,599	15.9%	1
Offense gravity (1–14)		—	—	1
Prior record	0	37,222	55.8%
	1–2	15,833	23.8%
	3–4	7119	10.7%
	5, REVOC, RFEL	6501	9.8%
CIP	—	13,126	19.7%
Supervision length (months)	—	—	—	16.02

Reported offense gravity score is a sample mode due to significant skew.

How Do Resentencing Rates Vary Between Probationers Who Are Substance-Involved Compared to Those With No Known Involvement?

Results from fixed effects logistic regressions for resentencing for a technical violation (Model 1) and resentencing for a new offense (Model 2) are presented in Table 2. Because odds ratios (ORs) can obscure absolute risk (Mood, 2010), we report average marginal effects (AMEs) in addition to traditional ORs. The AME is the average expected percent change in an individual’s predicted risk of the outcome (i.e., of being resentenced) given a one unit increase in the variable. For dichotomous variables, this is the difference in predicted odds of resentencing [for either a technical violation or a new offense] between those without the characteristic and those that possess it. For example, in Model 1, a male probationer’s odds of being resentenced for a technical violation is only about 90% of a female probationer’s odds of being resentenced for a technical violation.⁴ In this case, this difference in expected odds corresponds to an absolute likelihood of being resentenced for a technical violation that is 1% lower than the absolute likelihood of women (AME = −.010).

Table 2.

Multivariable Logistic Regression Results Predicting Resentencing for Technical Violations and New Offenses; County-Level Fixed Effects.

	Model 1 [P (Technical Violation)]				Model 2 [P (New Offense)]
	OR	AME	SE	Sig.	OR	AME	SE	Sig.
Male [female]	.901	−.010	.004	**	1.137	.005	.004	—
Black [white]	.911	−.009	.006		.923	−.003	.003	—
Age	.971	−.003	.000	***	.970	−.001	.000	***
Substance-involved [no known involvement]	1.339	.029	.010	***	1.278	.010	.006	—
Total supervision (mos.)	.999	.000	.000		1.003	.000	.000
CIP sentence [probation only]	1.326	.028	.009	***	.809	−.008	.006
Prior record score
[0]
1–2	1.327	.028	.004	***	1.286	.010	.004	*
3–4	1.457	.039	.007	***	1.607	.020	.006	***
5 or higher	1.548	.046	.008	***	1.497	.017	.009	*
Most serious offense
Property	1.293	.031	.005	***	1.445	.019	.008	*
Drugs	.827	−.020	.012		.818	−.008	.007
DUI	.488	−.063	.013		.331	−.032	.005	***
Other type [violent]	.840	−.018	.006	***	.806	−.009	.004	*
Felony [misdemeanor]	.988	−.001	.011		1.019	.001	.004
N	66,675				47,747

Note. OR = odds ratio; AME = average marginal effect; SE = standard error of the marginal effect. Model 2 includes fewer cases due to the exclusion of counties in which no new offenses were reported.

* p <.05 (two-tailed) ** p < .01 (two-tailed) *** p <. 005 (two-tailed).

Substance-involved offenders are more likely to be resentenced for a technical violation relative to those without known substance involvement (OR = 1.339). To put this in context, individuals who have no known substance-involvement have an average predicted probability of being resentenced for a technical violation of .107, compared to a predicted probability of .140 for those who are substance-involved (see Figure 1). Individuals who are substance-involved are not, however, significantly more likely to be resentenced for new offenses.⁵

We did not observe any significant differences by race for either the likelihood of technical violations or new offenses.⁶ Age is negatively associated with resentencing, with each additional year associated with an absolute 0.3% reduction (AME = −.003) in the likelihood of being resentenced for a technical violation and a 0.1% reduction in the probability of being resentenced for a new offense (AME = −.001) while under supervision. Individuals with prior criminal involvement are more likely to be resentenced for technical violations, but we do not observe a monotonically positive relationship between prior record and resentencing (the reference category is a prior record score of 0). Postestimation tests of coefficients show no significant differences. The odds that those sentenced to CIP are resentenced for technical violations is approximately 33% higher (an absolute difference of 2.8%), but not for new offenses. Compared to those convicted of person crimes, property offenders are more likely to be resentenced for technical violations and for new offenses. Additionally, the odds that individuals convicted of DUIs are resentenced for technical violations is 51% lower relative to probationers convicted of violent offenses; their odds of being resentenced for new offenses is 67% (corresponding to absolute differences of 6.3% and 3.2%).

How Does Treatment Affect Outcomes for Substance-Involved Probationers?

Table 3 displays the relationship between court-ordered treatment and the probability of both technical violations and new offenses; these models are conditional on being substance-involved. After controlling for individual differences, treatment is not associated with being resentenced for a technical violation. However, being ordered to treatment is associated with a 1.3% reduction in the probability of being resentenced for a new offense. As with the previous models, this AME is substantial considering that resentencing for new offenses is relatively rare (only 2.6% of substance-involved individuals). The absolute rate of resentencing for new offenses among those who are substance-involved but not ordered to treatment is 4.3%; an absolute reduction of 1.3% thus represents an approximately 30% lower risk of being resentenced for a new offense.⁷

Table 3.

Association Between Probation Outcomes and Court-Ordered Treatment; Substance-Involved Offenders Only.

Model	OR	AME	SE
P (technical violation)	.813	−.020	.016	—
P (new offense)	.724	−.013	.005	*

Note. Models also include controls for individual’s sex, age, race, prior record category, length of total supervision, whether the sentence involved a term of county intermediate punishment [CIP], primary offense type, and whether the primary offense was a felony or misdemeanor. OR = odds ratio; AME = average marginal effect; SE = standard error of the marginal effect.

* p < .05. ** p < .01. *** p < .005.

What Are the Consequences of Resentencing?

Given that substance-involved probationers are at an increased risk of being resentenced, it is helpful to consider the total and average costs of these events in terms of incarceration, community, and total supervision days. More than 60% of resentencing events for this population result in at least some term of incarceration, generally at the county-level. However, 42% of resentences also result in additional or extended probation terms, including 17.7% of those who are incarcerated (Table 4).

Table 4.

Most Serious Outcome of Resentencing and Imposition of Probation.

Sanction	N	Pct, %
Any incarceration	6,323	63.7
State	745	7.5
County	5,578	56.2
Any probation	4,470	45.0
With incarceration or county intermediate punishment	1,243	12.5
Probation alone	3,162	31.9
Other	440	4.4
Total	9,925	100.0

On average, the length of the incarceration sentences imposed on substance-involved offenders at resentencing is approximately 4 months (Table 5). The average probation sentence is more than four times longer. Accounting for all forms of supervision, resentenced individuals generally face an additional 10 months of criminal justice contact. Across all of the substance-involved probationers in our sample, this amounted to over 45,000 additional months. Individuals who committed only technical violations accounted for more than 76% of these months.

Table 5.

Additional Criminal Justice Supervision Resulting From Violations of Community Supervision By Substance-Involved Offenders, in Months.

	All Resentences		Technical Violations Only
	Mean	Total	Mean	Total	% of Total Resentences
Incarceration	4.0	12,702.5	3.8	9940.3	78.3
Probation	17.0	33,071.4	16.0	24,958.4	75.5
Total supervision	9.8	45,773.9	9.2	34,898.7	76.2

Discussion

More than half of all individuals under justice system supervision are supervised in the community. While precise estimates are hard to find, the rate of substance use disorders among those under community supervision is between four and nine times the rate among the general population, depending on the substance (Fearn et al., 2016; Substance Abuse and Mental Health Services Administration, 2020). Continued substance use has been associated with negative probation outcomes, yet treatment resources remain limited among probation departments (Taxman et al., 2007). This study explored the extent of substance involvement among individuals under community supervision in Pennsylvania, how that substance use related to probation outcomes, and the consequences of resentencing among substance-involved probationers. We also considered the extent to which treatment for substance-involved persons may improve outcomes.

We found that more than half of all individuals under community supervision in Pennsylvania are substance-involved. There were few substantial demographic or sentencing factor differences between probationers who were substance-involved compared to those with no known substance involvement. Yet, substance-involved individuals were more likely to be resentenced for a technical violation over the course of their community supervision. Some of these technical violations may have been the result of substance use directly (such as being unable or unwilling to abstain from substance use), though substance use also complicates other requirements of probation supervision such as maintaining employment (Dunigan et al., 2013; Sigurdsson et al., 2012) and housing (Tsai & Huang, 2019). Unfortunately, we are unable to separate these possible processes as too many jurisdictions did not provide sufficient detail on the reason for resentencing, prohibiting us from disaggregating technical violations. It is also possible that substance-involved individuals are subject to particularly stringent conditions of supervision: Eno Louden and Skeem (2013) found that probation officers perceive substance-involved probationers as being more at risk for technical violations, which can directly influence their surveillance of these cases. Consequently, it may be that probation officer perceptions create a self-fulfilling prophecy that reinforces those initial perceptions. Substance-involvement already presents additional challenges and increased risk of probation revocation (Johnson & Jones, 1998; Olson & Lurigio, 2000; Stevens-Martin et al., 2014). If probation officers are using their discretion as street level bureaucrats to provide a “shorter leash” for substance-involved probationers, these challenges—and subsequent rates of revocation—will only be intensified (Viglione, 2019).

The possibility of closer supervision of substance-involved persons is especially problematic when combined with other intense forms of surveillance. Substance-involved persons in our sample were more likely to be sentenced to CIP, a more restrictive community-based sanction than probation alone. This is important given the substantial increase in risk of technical violation resentencing among those sentenced to more intensive supervision levels (Grattet, Lin, & Petersilia, 2011; Grattet & Lin, 2016). Unsurprisingly, we found that CIP sentencing increased the risk of technical violations among our sample, above and beyond challenges faced by substance-involved individuals. While this is an unsurprising effect of heightened levels of surveillance in CIP, a larger proportion of substance-involved probationers are being exposed to this increased risk of technical violation resentencing. Considering we found that substance-involved probationers are already at increased risk of resentencing for technical violations, the convergence of CIP sentencing and substance involvement risk levels is important. Additionally, CIP sentences are rarely rehabilitation focused, with the most common forms in our sample being electronic monitoring and house arrest, and therefore lack treatment benefits to outweigh surveillance risks. Interestingly, among substance-involved individuals on probation, court-ordered treatment was not associated with increased risks of technical violations, relative to those who were not ordered to receive treatment, despite increased surveillance. It is possible that any potential surveillance effects are matched—and thus effectively canceled—by positive effects of treatment. However, this goes beyond the scope of this study.

In our statewide Pennsylvania sample, resentencing for community supervision violations most often results in incarceration and/or substantial extensions of probation. We conducted additional multivariate analyses (see supplemental material) that predicted the use of incarceration at resentencing and the length of imposed criminal justice supervision time. We found no evidence that substance-involved offenders were more or less likely to be incarcerated or that they receive longer or shorter sentences net of other relevant factors, namely, the type of violation (technical, new offense, or both), the intensity of supervision, and the original offense type. This is consistent with the argument that substance-involved offenders are not necessarily treated differently than their non-substance-involved peers but that substance involvement itself creates challenges for community supervision that then leads to substantially longer involvement with the justice system and—in many cases—a period of incarceration.

Policy Implications

The high rates of substance use among individuals involved with the justice system are deeply problematic on many fronts. In this study, we highlighted one such challenge by focusing on negative probation outcomes. In finding that substance-involved individuals were more likely to be resentenced for technical violations but not new offenses, our results strongly suggest that substance-involved individuals are not necessarily of greater risk to public safety than individuals without known substance use. Instead, when these individuals struggle on probation, these violations appear to result from failing to abstain from drugs and/or alcohol, failure to keep appointments, attain employment, or keep housing. Each of these might be interpreted as a symptom of unmet treatment need, especially given that violations related to employment, housing, and appointments may stem from continued substance involvement. The frequent (and costly) use of incarceration in response to these violations may do little to improve public safety while placing individuals in contexts even less likely to have adequate treatment resources (Friedmann, Taxman, & Henderson, 2007).

Yet the problem is bigger than the recommendation that probation officers should simply use treatment more often. In many jurisdictions, agencies are faced with ever-growing lists of individuals in need of resources and budgets that either grow too slowly or, worse yet, shrink. Individuals at these agencies are constantly trying to do more with less (Van Deinse, Cuddeback, Wilson, & Burgin, 2018). The result, unfortunately, sometimes means that decisions are made to service more people at the expense of treatment fidelity, ultimately undermining the efficacy of those treatments. Those in charge of facilitating treatment for justice-involved populations also face additional barriers in local resistance to adopting the full range of evidence-based treatments for particular populations, such as agonist or partial agonist therapies for opioid use disorders or abstinence incentive programs (Grella, Ostile, Scott, Dennis, & Carnavale, 2020; Matusow et al., 2013). Rural areas may also face particular hurdles in providing sufficient access to evidence-based treatment options for all clients (Moody, Satterwhite, & Bickel, 2017).

While increased public substance use treatment funding would arguably go a long way toward addressing many of these issues, we are not overly optimistic at a drastic increase in rehabilitative spending. Instead (or in addition), we would urge probation officers and others responsible for supervising individuals in the justice system to reconsider the value of technical violations to the broader mission of public safety, especially as related to continued substance use. Substance-involved probationers present particular challenges; abstinence from substances is a condition of probation, yet recovery from substance dependence is a lengthy process that is typically punctuated by relapses (Bell et al., 2006; Mclellan, Lewis, O’brien, & Kleber, 2000; Nosyk et al., 2013). Substance dependence produces “significant and lasting changes in brain chemistry and function” (Mclellan et al., 2000, p. 1689). Given these physiological changes, the low rates of cessation, and the average time to cessation being decades long (Dennis et al., 2005; Hser et al., 2015; Nosyk et al., 2013), it is probable that many substance-involved probationers will struggle to maintain abstinence during their supervision, especially if they are not provided high quality, evidence-based treatment of adequate length and intensity.

Individuals who are substance-involved in our sample were no more likely to be resentenced for a new offense, though they did face a substantial increase in the risk of being resentenced for a technical violation.⁸ Prior research suggests that there is little correlation between risk for technical violations and risk for new offenses (Campbell, 2016). Together, this suggests that we are resentencing, incarcerating, and prolonging supervision of substance-involved probationers who present minimal risk to the public. Finally, it is important to consider that incarceration can come with steep costs, especially for those who are substance-involved; individuals released from incarceration are at a greater risk of death in the first 30 days, primarily attributable to an increased risk of overdose death (Binswanger, Blatchford, Mueller, & Stern, 2013; Merrall et al., 2010; Møller et al., 2010). What, then, is the value added of incarceration in response to technical violations, especially for continued substance use? How is public safety benefited? To what extent are individual trajectories and outcomes positively altered if both substance use and criminal justice involvement continue? This will require—in some offices—radical change in the culture of probation, a shift from risk management back toward a more holistic approach. A shift to the development of rapport and positive relationships between parole officers and those under supervision has proven to reduce recidivism and enhance outcomes (Kennealy, Skeem, Manchak, & Eno Louden, 2012; Skeem, Louden, Polaschek, & Camp, 2007). Additionally, we need to shift the perception that substance-involved individuals must inherently be watched more closely than others (Eno Louden & Skeem, 2013).

Our models point to a similar problem with individuals participating in (largely surveillance oriented) intermediate punishment programs—while they were more likely to be resentenced for a technical violation, they were not at an increased risk of being resentenced for a new offense. The effects of increased supervision levels have been studied in many domains of the criminal justice system, and prior research has illustrated that the resulting technical violations often do not align with other measures of recidivism or with risk of continued criminal behavior (Campbell, 2016; Ostermann, Salerno, & Hyatt, 2015). Hamilton and Campbell (2013) label so-called “failure” due to non-criminal revocations (i.e., technical violations) the “dark figure” of community corrections. While they are focused on technical violations stemming from halfway house placement, the impact of increased surveillance on non-criminal revocations is present in many community corrections studies (Bell et al., 2014; Clark, 2016; Duwe, 2015).

Limitations

While our analyses are among few statewide assessments of probation outcomes and take important steps to understanding the outcomes of substance-involved probationers, they are not without limitations. Data quality was variable across county, although the removal of low quality data counties from our analyses strengthens our confidence in our results. Future work should strive to unpack this variation by county by focusing on how community factors such as differences in substance using populations, different substances of abuse, and differential enforcement affect probation outcomes across jurisdictions. Additionally, our analyses were limited by the information available regarding the substance involvement of the sample. Ideally, we would have a better understanding of the severity of substance use, substance(s) of abuse, and type and quality of treatment provided. The PCS is already in the process of redesigning their data collection instrument to better collect information about the reasons for resentencing and the role of substance involvement in those proceedings. However, our identification strategy, which likely may not identify many offenders who use substances but are convicted of other offenses, likely provides a conservative base estimate of these issues. Finally, this work cannot account for differences in the likelihood of a formal resentencing hearing being initiated. Future studies should strive to account for potential differences across probation officers and more direct measures of violations.

Conclusion

Substance-involved probationers present unique challenges given the physical nature of dependence, low rates of long-term cessation, and abstinence from substances as a condition of community supervision. We found that substance-involved probationers in Pennsylvania are at increased risk for resentencing due to technical violations but not for new offenses. Adjusted sentences for violations of community supervision came at a substantial cost for individuals, averaging more than three months of total additional justice contact for a technical violation without new offenses. Policymakers, probation departments, and treatment providers must be cognizant of the potential net widening of surveillance as they consider potential responses to non-criminal violations of supervision conditions. With substance-involved probationers, an understanding of the typical, lengthy path of recovery should inform sound policies, with responses to relapse among probationers contributing to the long-term success of the individual.

Footnotes

Author’s Note

The findings in this article represent the views of the authors and not the official position of the Pennsylvania Commission on Sentencing.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by a grant from Vital Strategies.

ORCID iD

Miranda A. Galvin

Notes

Author Biographies

Miranda A. Galvin is Postdoctoral Scholar in Sentencing at the Criminal Justice Research Center at Pennsylvania State University where she works with the Pennsylvania Commission on Sentencing. Her research considers issues related to white-collar crime, criminal prosecution and sentencing, and issues related to substance use treatment for justice-involved populations.

Kimberly M. Davidson is Assistant Professor in the College of Criminology & Criminal Justice at Florida State University. Her research interests include corrections, programming, and rehabilitation, community reentry, and substance use.

Matthew Kleiman, Ph.D., is the Deputy Director of the Pennsylvania Commission on Sentencing. His research focuses on advancing effective, humane, and rational policies that promote fairer and more uniform decisions at pretrial, sentencing, resentencing, and parole, including the identification and promulgation of evidence-based practices.

References

Albonetti

C. A.

Hepburn

J. R.

(1997). Probation revocation: A proportional hazards model of the conditioning effects of social disadvantage. Social Problems, 44(1), 124–138. https://doi.org/10.1525/sp.1997.44.1.03x0216h

Alper

Ruhland

(2016). Probation revocation and its causes: Profiles of state and local jurisdictions. Ramsey County, MN: Robina Institute of Criminal Law and Criminal Justice.

Bell

Bucklen

K. B.

Nakamura

Tomkiel

Santore

Russell

Orth

(2014). Pennsylvania department of corrections recidivism report, 2013. Harrisburg, PA: Pennsylvania Department of Corrections.

Bell

Burrell

Indig

Gilmour

(2006). Cycling in and out of treatment; Participation in methadone treatment in NSW, 1990-2002. Drug and Alcohol Dependence, 81(1), 55–61. https://doi.org/10.1016/j.drugalcdep.2005.05.010

Bennett

Holloway

Farrington

(2008). The statistical association between drug misuse and crime: A meta-analysis. Aggression and Violent Behavior, 13(2), 107–118. https://doi.org/10.1016/j.avb.2008.02.001

Binswanger

I. A.

Blatchford

P. J.

Mueller

S. R.

Stern

M. F.

(2013). Mortality after prison release: Opioid overdose and other causes of death, risk factors, and time trends from 1999 to 2009. Annals of internal medicine, 159(9), 592–600. https://doi.org/10.7326/0003-4819-159-9-201311050-00005

Bondurant

S. R.

Lindo

J. M.

Swensen

I. D.

(2018). Substance abuse treatment centers and local crime. Journal of Urban Economics, 104, 124–133. https://doi.org/10.1016/j.jue.2018.01.007

Bronson

Carson

E. A.

(2019). Prisoners in 2017. Washington, DC: Bureau of Justice Statistics.

Bronson

Stroop

Zimmer

Berzofsky

(2017). Drug use, dependence, and abuse among state prisoners and jail inmates, 2007-2009. US Department of Justice, Office of Justice Programs, Bureau of Justice Statistics. (Issue June).

10.

Campbell

C. M.

(2016). It’s not technically a crime. Criminal Justice Policy Review, 27(7), 643–667. https://doi.org/10.1177/0887403414553098.

11.

Caulkins

J. P.

Reuter

Blumstein

Bouchard

Cook

Humphreys

Sevigny

(2019). Dealing more effectively and humanely with illegal drugs. Crime & Justice, 46(1), 95–156. https://doi.org/10.1086/688458.

12.

Clark

V. A.

(2016). Predicting two types of recidivism among newly released prisoners. Crime & Delinquency, 62(10), 1364–1400. https://doi.org/10.1177/0011128714555760

13.

Cohen

R. L.

(1995). Probation and parole violators in state prison, 1991. Washington, DC: Bureau of Justice Statistics.

14.

Council for State Governments (2019). Confined and costly.

15.

Dennis

M. L.

Scott

C. K.

Funk

Foss

M. A.

(2005). The duration and correlates of addiction and treatment careers. Journal of Substance Abuse Treatment, 281(2 SUPPL), S51–S62. https://doi.org/10.1016/j.jsat.2004.10.013

16.

Drake

E. K.

(2011). What works. In community supervision. Olympia, WA: Washington: Institute for Public Policy.

17.

Dunigan

Acevedo

Campbell

Garnick

D. W.

Horgan

C. M.

Huber

Lee

M. T.

Panas

Ritter

G. A.

(2013). Engagement in outpatient substance abuse treatment and employment outcomes. The Journal of Behavioral Health Services & Research, 41(1), 20–36. https://doi.org/10.1007/s11414-013-9334-2.

18.

Duwe

(2015). An outcome evaluation of a prison work release program. Criminal Justice Policy Review, 26(6), 531–554. https://doi.org/10.1177/0887403414524590

19.

Eno Louden

Skeem

J. L.

(2013). How do probation officers assess and manage recidivism and violence risk for probationers with mental disorder? An experimental investigation. Law and Human Behavior, 37(1), 22–34. https://doi.org/10.1037/h0093991

20.

Evans

Longshore

Prendergast

Urada

(2006). Evaluation of the substance abuse and crime prevention act: Client characteristics, treatment completion and Re-offending three years after implementation. Journal of Psychoactive Drugs, xxx(3), 357–367. https://doi.org/10.1080/02791072.2006.10400599

21.

Fearn

N. E.

Vaughn

M. G.

Nelson

E. J.

Salas-Wright

C. P.

DeLisi

Qian

(2016). Trends and correlates of substance use disorders among probationers and parolees in the United States 2002–2014. Drug and Alcohol Dependence, 167, 128–139. https://doi.org/10.1016/j.drugalcdep.2016.08.003

22.

Friedmann

P. D.

Taxman

F. S.

Henderson

C. E.

(2007). Evidence-based treatment practices for drug-involved adults in the criminal justice system. Journal of Substance Abuse Treatment, 32(3), 267–277. https://doi.org/10.1016/j.jsat.2006.12.020.

23.

Glaze

L. E.

Bonczar

T. P.

(2009). Probation and Parole in the United States , 2008 .

24.

Goldstein

P. J.

(1985). The drugs/violence nexus: A tripartite conceptual framework. Journal of Drug Issues, 15(4), 493–506. https://doi.org/10.1177/002204268501500406

25.

Grattet

Lin

(2016). Supervision intensity and parole outcomes: A competing risks approach to criminal and technical parole violations. Justice Quarterly, 33(4), 565–583. https://doi.org/10.1080/07418825.2014.932001

26.

Grattet

Lin

Petersilia

(2011). Supervision regimes, risk, and official reactions to parolee deviance. Criminology, 49(2), 371–399. https://doi.org/10.1111/j.1745-9125.2011.00229.x

27.

Grella

C. E.

Ostile

Scott

C. K.

Dennis

Carnavale

(2020). A scoping review of barriers and facilitators to implementation of medications for treatment of opioid use disorder within the criminal justice system. International Journal of Drug Policy, 81(1), 102768. https://doi.org/10.1016/j.drugpo.2020.102768.

28.

Hall

Bell

Carless

(1993). Crime and drug use among applicants for methadone maintenance. Drug and Alcohol Dependence, 31(2), 123–129. https://doi.org/10.1016/0376-8716(93)90064-w

29.

Hamilton

Z. K.

Campbell

C. M.

(2013). A dark figure of corrections. Criminal Justice and Behavior, 40(2), 180–202. https://doi.org/10.1177/0093854812464219

30.

Han

Compton

W. M.

Blanco

Colpe

L. J.

(2017). Prevalence, treatment, and unmet treatment needs of US adults with mental health and substance use disorders. Health Affairs, 36(10), 1739–1747. https://doi.org/10.1377/hlthaff.2017.0584

31.

Hildebrand

Hol

A. M.

Bosker

(2013). Predicting probation supervision violations. Psychology, Public Policy, and Law, 19(1), 114–125. https://doi.org/10.1037/a0028179

32.

Breaux

Jannetta

(2014). Examining racial disparities in the Sixth Judicial district of Iowa’s probation revocation outcomes. Washington, DC: Urban Institute.

33.

Holloway

K. R.

Bennett

T. H.

Farrington

D. P.

(2006). The effectiveness of drug treatment programs in reducing criminal behavior: A meta-analysis. Psicothema, 18(3), 620–629.

34.

Hser

Y. I.

Evans

Grella

Ling

Anglin

(2015). Long-term course of opioid addiction harvard review of psychiatry. Lippincott Williams and Wilkins, 23(2), 76–89. https://doi.org/10.1097/HRP.0000000000000052

35.

Hughes

T. A.

Wilson

D. J.

Beck

A. J.

(2001). Trends in State Parole, 1990-2000

36.

Hyatt

J. M.

Barnes

G. C.

(2014). An experimental evaluation of the impact of intensive supervision on the recidivism of high-risk probationers. Crime and Delinquency, 63(1), 3–38. https://doi.org/10.1177/0011128714555757

37.

Johnson

W. W.

Jones

(1998). Probation, race, and the war on drugs: An empirical analysis of drug and non-drug felony probation outcomes. Journal of Drug Issues, 28(4), 985–1004. https://doi.org/10.1177/002204269802800409

38.

Kaeble

(2018). Probation and parole in the United States, 2016. Washington, DC: Bureau of Justice Statistics.

39.

Kaeble

Alper

(2020). Probation and Parole in the United States , 2017

40.

Kaeble

Bonczar

T. P.

(2016). Probation and Parole in the United States , 2015

41.

Kennealy

P. J.

Skeem

J. L.

Manchak

S. M.

Eno Louden

(2012). Firm, fair, and caring officer-offender relationships protect against supervision failure. Law and Human Behavior, 36(6), 496–505. https://doi.org/10.1037/h0093935

42.

Krawczyk

Picher

C. E.

Feder

K. A.

Saloner

(2017). Only one in twenty justice-referred adults in specialty treatment for opioid use receive methadone or buprenorphine. Health Affairs, 36(12), 2046–2053. https://doi.org/10.1377/hlthaff.2017.0890.

43.

Krebs

Urada

Evans

Huang

Hser

Y. I.

Nosyk

(2017). The costs of crime during and after publicly funded treatment for opioid use disorders: A population-level study for the state of California. Addiction, 112(5), 838–851. https://doi.org/10.1111/add.13729

44.

Matusow

Dickman

S. L.

Rich

J. D.

Fong

Dumont

D. M.

Hardin

Marlowe

Rosenblum

(2013). Medication assisted treatment in US drug courts: Results from a nationwide survey of availability, barriers and attitudes. Journal of Substance Abuse Treatment, 44(5), 473–480. https://doi.org/10.1016/j.jsat.2012.10.004

45.

Mclellan

A. T.

Lewis

D. C.

O’brien

C. P.

Kleber

H. D.

(2000). Drug dependence, a chronic medical illness implications for treatment, insurance, and outcomes evaluation. JAMA, 284(13), 1689–1695. https://doi.org/10.1001/jama.284.13.1689

46.

Merrall

E. L. C.

Kariminia

Binswanger

I. A.

Hobbs

M. S.

Farrell

Marsden

Bird

S. M.

(2010). Meta-analysis of drug-related deaths soon after release from prison. Addiction, 105(9), 1545–1554. https://doi.org/10.1111/j.1360-0443.2010.02990.x.

47.

Minor

K. L.

Wells

J. B.

Sims

(2003). Recidivism among federal probationers-predicting sentence violation. Federal Probation, 67(1), 31–36.

48.

Minton

T. D.

Beatty

L. G.

Zeng

(2020). Correctional populations in the United States, 2019, statistical tables. Washington, DC: Bureau of Justice Statistics.

49.

Mitchell

K. L.

Reitz

K. R.

Watts

Ellis

C. A.

(2014). Profiles in probation revocation: Examining the legal framework in 21 states. Minneapolis, MN: Robina Institute of Criminal.

50.

Møller

L. F.

Matic

van den Bergh

B. J.

Moloney

Hayton

Gatherer

(2010). Acute drug-related mortality of people recently released from prisons. Public Health, 124(11), 637–639. https://doi.org/10.1016/j.puhe.2010.08.012.

51.

Mood

(2010). Logistic regression: Why we cannot do what we think we can do, and what we can do about it. European Sociological Review, 26(1), 67–82. https://doi.org/10.1093/esr/jcp006

52.

Moody

Satterwhite

Bickel

W. K.

(2017). Substance use in rural central appalachia: Current status and treatment considerations. Rural Mental Health, 41(2), 123–135. https://doi.org/10.1037/rmh0000064.

53.

Morash

Kashy

D. A.

Smith

S. W.

Cobbina

J. E.

(2019). Technical Violations, Treatment and Punishment Responses, and Recidivism of Women on Probation and Parole. Criminal Justice Policy Review, 30(5), 788–810. https://doi.org/10.1177/0887403417723425

54.

Morgan

K. D.

(1995). Variables associated with successful probation completion. Journal of Offender Rehabilitation, 22(3–4), 141–153. https://doi.org/10.1300/j076v22n03_11

55.

Mumola

C. J.

Bonczar

T. P.

(1998). Substance abuse and treatment of adults on probation, 1995. Washington, DC: U.S. Department of Justice, Office of Justice Programs, Bureau of Justice Statistics.

56.

Nosyk

Anglin

M. D.

Brecht

M. L.

Lima

V. D.

Hser

Y. I.

(2013). Characterizing durations of heroin abstinence in the California civil addict program: Results from a 33-year observational cohort study. American Journal of Epidemiology, 177(7), 675–682. https://doi.org/10.1093/aje/kws284

57.

Olson

D. E.

Alderden

Lurigio

A. J.

(2003). Men are from mars, women are from venus, but what role does gender play in probation recidivism?. Justice Research and Policy, 5(2), 33–54. https://doi.org/10.3818/jrp.5.2.2003.33

58.

Olson

D. E.

Lurigio

A. J.

(2000). Predicting probation outcomes: Factors associated with probation rearrest, revocations, and technical violations during supervision. Justice Research and Policy, 2(1), 73–86. https://doi.org/10.3818/jrp.2.1.2000.73

59.

Ostermann

Salerno

L. M.

Hyatt

J. M.

(2015). How different operationalizations of recidivism impact conclusions of effectiveness of parole supervision. Journal of Research in Crime and Delinquency, 52(6), 771–796. https://doi.org/10.1177/0022427815580626

60.

Owens

G. P.

Rogers

S. M.

Whitesell

A. A.

(2011). Use of mental health services and barriers to care for individuals on probation or parole. Journal of Offender Rehabilitation, 50(1), 37–47. https://doi.org/10.1080/10509674.2011.536721.

61.

Persoskie

Ferrer

(2017). A most odd ratio: Interpreting and describing odds ratios. American Journal of Preventive Medicine, 52(2), 224–228. https://doi.org/10.1016/j.amepre.2016.07.030

62.

Petersilia

Turner

(1993). Intensive probation and parole. Crime and Justice, 17, 281–335. https://doi.org/0.1086/449215.

63.

Phelps

M. S.

(2013). The paradox of probation: Community supervision in the age of mass incarceration. Law & Society, 35(1–2), 51–80. https://doi.org/10.1111/lapo.12002

64.

Phelps

M. S.

(2018). Mass probation and inequality: Race, class, and gender disparities in supervision and revocation. In Ulmer

J. T.

Bradley

M. S.

(Eds.), Handbook on punishment decisions (pp. 43–65). New York: Routledge.

65.

Phelps

M. S.

(2020). Mass probation from micro to macro: Tracing the expansion and consequences of community supervision. Annual Review of Criminology, 3(1), 261–279. https://doi.org/10.1146/annurev-criminol-011419-041352.

66.

Salisbury

E. J.

Van Voorhis

(2009). Gendered pathways: A quantitative investigation of women probationers’ paths to incarceration. Criminal Justice and Behavior, 36(6), 541–566. https://doi.org/10.1177/0093854809334076

67.

Saloner

Bandara

S. N.

McGinty

E. E.

Barry

C. L.

(2016). Justice-involved adults with substance use disorders: Coverage increased but rates of treatment did not in 2014. Health Affairs, 35(6), 1058–1066. https://doi.org/10.1377/hlthaff.2016.0005

68.

Sigurdsson

S. O.

Ring

B. M.

O’Reilly

Silverman

(2012). Barriers to employment among unemployed drug users: Age predicts severity. The American Journal of Drug and Alcohol Abuse, 38(6), 580–587. https://doi.org/10.3109/00952990.2011.643976

69.

Simpson

S. S.

Yahner

J. L.

Dugan

(2008). Understanding women’s pathways to jail: Analysing the lives of incarcerated women. Australian & New Zealand Journal of Criminology, 41(1), 84–108. https://doi.org/10.1375/acri.41.1.84

70.

Skeem

J. L.

Louden

J. E.

Polaschek

Camp

(2007). Assessing relationship quality in mandated community treatment: Blending care with control. Psychological Assessment, 19(4), 397–410. https://doi.org/10.1037/1040-3590.19.4.397

71.

Steinmetz

K. F.

Henderson

(2015). On the precipice of intersectionality: The influence of race, gender, and offense severity interactions on probation outcomes. Criminal Justice Review, 40(3), 361–377. https://doi.org/10.1177/0734016815577194.

72.

Steinmetz

K. F.

Henderson

(2016). Inequality on probation: An examination of differential probation outcomes. Journal of Ethnicity in Criminal Justice, 14(1), 1–20. https://doi.org/10.1080/15377938.2015.1030527

73.

Stevens-Martin

Oyewole

Hipolito

(2014). Technical revocations of probation in one jurisdiction: Uncovering hidden realities. Federal Probation, 78(3), 16–20.

74.

Stickels

J. W.

(2007). A study of probation revocations for technical violations in Hays County, Texas, USA. Probation Journal, 54(1), 52–61. https://doi.org/10.1177/0264550507073328.

75.

Substance Abuse and Mental Health Services Administration (2020). Substance abuse and mental health services adminsitration. Washington, DC: U.S. Department of Health & Human Services.

76.

Tapia

Harris

P. M.

(2006). Race and revocation: Is there a penalty for young, minority males? Journal of Ethnicity in Criminal Justice, 4(3), 1–25. https://doi.org/10.1300/j222v04n03_01

77.

Taxman

F. S.

Perdoni

M. L.

Harrison

L. D.

(2007). Drug treatment services for adult offenders: The state of the state. Journal of Substance Abuse Treatment, 32(3), 239–254. https://doi.org/10.1016/j.jsat.2006.12.019

78.

Hayhurst

K. P.

Leitner

Davies

Millar

Jones

Flentje

Shaw

(2019). The effectiveness of diversion programmes for offenders using Class A drugs: A systematic review and meta-analysis. Drugs: Education Prevention and Policy, 26(2), 1–2. https://doi.org/10.1080/09687637.2017.1398715

79.

The Pew Charitable Trusts . (2018). Probation and parole systems marked by high Stakes, missed opportunities. Philadelphia, PA: The Pew Charitable Trusts.

80.

Tripodi

S. J.

Pettus-Davis

(2013). Histories of childhood victimization and subsequent mental health problems, substance use, and sexual victimization for a sample of incarcerated women in the US. International Journal of Law and Psychiatry, 36(1), 30–40. https://doi.org/10.1016/j.ijlp.2012.11.005

81.

Tsai

(2019). Utilization of addiction treatment among US adults with history of incarceration and substance use disorders. Addiction Science & Clinical Practice, 14(1), 9. https://doi.org/10.1186/s13722-019-0138-4

82.

Tsai

Huang

(2019). Systematic review of psychosocial factors associated with evictions. Health and Social Care in the Community, 27(3), e1–e9. https://doi.org/10.1111/hsc.12619.

83.

Van Deinse

T. B.

Cuddeback

G. S.

Wilson

A. B.

Burgin

S. E.

(2018). Probation oficers’ perceptions of supervising probationers with mental illness in rural and urban settings. American Journal of Criminal Justice, 43(2), 267–277. https://doi.org/10.1007/s12103-017-9392-8

84.

Viglione

(2019). The Risk-Need-Responsivity Model: How Do Probation Officers Implement the Principles of Effective Intervention? Criminal Justice and Behavior, 46(5), 655-673.

85.

Volkow

N. D.

Koob

G. F.

McLellan

A. T.

(2016). Neurobiologic advances from the brain disease model of addiction. New England Journal of Medicine, 374(4), 363–371. https://doi.org/10.1056/NEJMra1511480

86.

Wodahl

E. J.

Ogle

Heck

(2011). Revocation trends: A threat to the legitimacy of community-based corrections. The Prison Journal, 91(2), 207–226. https://doi.org/10.1177/0032885511404356