Criminal recidivism among justice-involved veterans following substance use disorder residential treatment

Journal Pre-proofs Criminal Recidivism among Justice-Involved Veterans Following Substance Use Disorder Residential Treatment Daniel M. Blonigen, Kathryn S. Macia, David Smelson, Christine Timko PII: DOI: Reference:

S0306-4603(19)30849-4 https://doi.org/10.1016/j.addbeh.2020.106357 AB 106357

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Addictive Behaviors Addictive Behaviors

Received Date: Revised Date: Accepted Date:

13 July 2019 13 February 2020 13 February 2020

Please cite this article as: D.M. Blonigen, K.S. Macia, D. Smelson, C. Timko, Criminal Recidivism among Justice-Involved Veterans Following Substance Use Disorder Residential Treatment, Addictive Behaviors Addictive Behaviors (2020), doi: https://doi.org/10.1016/j.addbeh.2020.106357

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Recidivism among veterans 1 RUNNING HEAD: CRIMINAL RECIDIVISM AMONG JUSTICE-INVOLVED VETERANS

Criminal Recidivism among Justice-Involved Veterans Following Substance Use Disorder Residential Treatment

Daniel M. Blonigen, PhD,a,b,c Kathryn S. Macia, PhD,b David Smelson, PsyD,d,e and Christine Timko, PhDa,c

a

Center for Innovation to Implementation, VA Palo Alto Health Care System, 795 Willow Road (152-

MPD), Menlo Park, CA 94025, USA b

Clinical Psychology PhD Program, Palo Alto University, 1791 Arastradero Road, Palo Alto, CA

94304, USA c

Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401

Quarry Road, Palo Alto, CA 94304, USA d

Center for Health Care Organization and Implementation Research, Bedford VA Medical Center, 200

Springs Road, Bedford, MA 01730, USA e

University of Massachusetts Medical School, 55 Lake Ave N, Worcester, MA 01655, USA

Corresponding Author: Daniel M. Blonigen, Ph.D. 795 Willow Road (152-MPD) Menlo Park, CA 94025 Phone: 650-493-5000, ext. 27828 Fax: 650-617-2736 Email: [email protected]

Recidivism among veterans 2 Abstract Veterans in treatment for substance use disorders (SUD) often report past criminal offending. However, the rate of criminal recidivism in this population is unknown. Further, prior research in veterans has not examined personality factors as predictors of recidivism, despite the prominence of such factors in leading models of recidivism risk management. We examined these issues in a secondary data analysis of 197 military veterans with a history of criminal offending who were enrolled in an SUD residential treatment program. Participants were interviewed using several measurement instruments at treatment entry, one month into treatment, treatment discharge, and 12 months post-discharge. Most veterans (94%) had a history of multiple charges, and 53% had recent involvement in the criminal justice system at the time of treatment entry. In the 12 months postdischarge, 22% reported reoffending. In addition, 30% of patients who had been recently involved in the criminal justice system at treatment entry reoffended during follow-up. Higher friend relationship quality (OR=2.32, 95% CI [1.03, 5.21]) at treatment entry and higher staff ratings of patients’ relationship quality with other residents during treatment (OR=2.76, 95% CI [1.40, 5.41]) predicted lower odds of recidivism post-discharge. After accounting for these factors, smaller reductions during treatment in the personality trait of Negative Emotionality predicted an increased risk for criminal recidivism post-discharge (OR=1.13, 95% CI [1.01, 1.26]). Results support augmenting the curriculum of SUD programs for veterans with services aimed at reducing risk for criminal recidivism, with a focus on interventions that directly target patients’ social support networks and tendencies towards negative emotionality.

Keywords: Veterans; Substance Use Disorder; Residential Treatment; Criminal Recidivism; Personality

Recidivism among veterans 3 1. Introduction 1.1.

Substance use disorders and criminal justice involvement among veterans Substance use disorders (SUD) are prevalent among veterans of the U.S. military (Humphreys,

Wagner, & Gage, 2011). For example, among veterans of conflicts in Iraq and Afghanistan who received care in the Veterans Health Administration (VHA), approximately 11% met criteria for an SUD diagnosis (Seal et al., 2011). Within the veteran population, those involved in the criminal justice system have the highest rates of SUD (Blodgett et al., 2015); among veterans in jails or treatment courts who initiated VHA care, 58% of women and 72% of men were diagnosed with an SUD (Finlay et al., 2015). Further, within incarcerated settings, veterans have higher rates of SUDs than nonveterans (Bronson, Carson, & Noonan, 2015). There is a strong link between SUD and criminal justice involvement among veterans in SUD treatment programs. Weaver, Trafton, Kimerling, Timko, and Moos (2013) examined this issue in a large, nationally representative sample of veterans in SUD treatment programs in VHA. A sizeable majority of patients (85%) had at least one criminal charge in their lifetime, with 58% reporting at least three charges and 46% reporting at least one criminal conviction. Notably, these rates of criminal histories are higher than many prior studies of SUD patients in general (Diehl, Pillon, Dos Santos, Rassool, & Laraniera, 2016; Marel, Mills, Darke, Ross, Burns, & Teesson, 2015; Theriot & Segal, 2005). Extensive criminal histories have also been reported in other studies of veterans in SUD treatment, including an outpatient treatment sample (Schultz, Blonigen, Finlay, & Timko, 2015) and those with a history of trauma exposure (Bennett, Morris, Sexton, Bonar, & Chermack, 2017). Across these studies, the profile of lifetime criminal charges among veterans in SUD treatment is not limited to substance-specific offenses and includes both violent and non-violent charges. For example, Weaver et al. (2013) observed that most veterans in SUD treatment (69%) report at least one arrest that

Recidivism among veterans 4 was not for possession or sale of an illicit drug or for public intoxication or driving under the influence of drugs or alcohol. 1.2.

Criminal recidivism among veterans in SUD treatment Among justice-involved adults in general, recidivism is the norm rather than the exception

(Durose, Cooper, & Snyder, 2014). Like their non-veteran counterparts, many justice-involved veterans are also caught in a cycle of contact with the criminal justice system. Among veterans in jails and prisons, 68% and 73%, respectively, had at least one prior episode of incarceration (Bronson et al., 2015). Consistent with this, data from VHA’s Veterans Justice Program indicate that veterans served by this program have an average of eight arrests in their lifetime (Department of Veterans Affairs, 2012). In studies of justice-involved veterans, examinations of the rate and predictors of criminal recidivism have been limited to samples of treatment court participants. For example, in a national sample of veterans who entered a Veterans Treatment Court from 2011 to 2015, 14% were reincarcerated within 11 months of court entry. Further, risk for reincarceration was significantly higher among those with a prior episode of incarceration and an SUD diagnosis (Tsai, Finlay, Flatley, Kasprow, & Clark, 2018). By contrast, among veterans in SUD treatment with a history of criminal justice involvement, the rate and predictors of criminal recidivism following discharge from treatment are unknown. Such information can inform the development and testing of interventions to reduce risk for recidivism in this population. Further, community-based SUD treatment programs, if targeting criminogenic risk factors in addition to substance use problems, may function as secondary prevention and early-intercept diversion for future involvement with the criminal justice system. The importance of examining these issues among veterans per se is highlighted by differences in the sociodemographics and offense profile of justice-involved veterans relative to non-veterans – e.g.,

Recidivism among veterans 5 justice-involved veterans tend to be older, more educated, and more likely to have a history of violent charges (Bronson et al., 2015). 1.3.

Predictors of criminal recidivism among SUD treatment samples Although there is no prior research on predictors of recidivism among veterans in SUD

treatment, candidate variables may include established predictors of recidivism in other populations. These are based on studies that used diverse criterion variables for operationalizing recidivism. For example, variables in the domain of criminal history, such as the extent and recency of criminal justice involvement, are robust predictors of recidivism risk in the correctional literature (Andrews & Bonta, 2010; Olver, Stockdale, & Wormith, 2011). Other candidates include variables in the domain of functioning and treatment processes that are linked to criminal justice involvement or antisocial behavior in this population. For example, substance use and psychiatric severity have both been linked to criminal justice involvement in veterans (Blonigen et al., 2016b; Blonigen, King, & Timko, in press). Regarding the latter, symptoms of antisocial personality disorder (ASPD) are more common among incarcerated than non-incarcerated veterans (Black et al., 2005; Shaw, Churchill, Noyes, & Loeffelholz, 1987), and posttraumatic stress disorder (PTSD) was found to be associated with postdeployment arrests in a national sample of veterans returning from Iraq and Afghanistan (Elbogen et al., 2012a). Poor social support, including lack of peer support for quitting alcohol and drugs, is an established predictor of SUD treatment outcomes in veterans (Moos, 2007) and has been shown to predict past-year aggression among Iraq/Afghanistan returnees (Elbogen et al., 2012b). In terms of treatment processes, lower relationship quality with other SUD patients, lower satisfaction with treatment, and poor treatment retention are all associated with substance use relapse in veterans (e.g., Gifford, Ritsher, McKellar, & Moos, 2006; Harris, McKellar, Moos, & Schaefer, 2006; Hser, Evans, Huang, & Anglin, 2004) and have been found to predict criminal recidivism after discharge among

Recidivism among veterans 6 probationers in SUD treatment (Broome, Knight, Hiller, & Simpson, 1996; Broome, Knight, Knight, Hiller, & Simpson, 1997; Hiller, Knight, & Simpson, 1999). In contrast with variables in the domains of criminal history and functioning/treatment processes, prior research has not examined normal-range personality factors as predictors of criminal recidivism in SUD treatment samples. Normal-range personality is defined as individuals’ typical patterns of thinking, feeling, and behaving. They are indexed via broad-band inventories that measure a range of specific traits that cohere into higher-order, global dimensions. For example, the Multidimensional Personality Questionnaire (Tellegen, 2000) is marked by higher-order factors of Positive Emotionality (a tendency to experience positive emotions through active engagement in one’s environment and close relationships with others), Negative Emotionality (a propensity to experience a range of negative emotions such as anxiety, mistrust, and anger), and Constraint (a tendency to be cautious, planful, and risk avoidant). The dearth of research on personality factors as predictors of criminal recidivism in SUD treatment samples is a significant gap in this literature, given that such factors have been shown to predict a range of other SUD treatment outcomes (Blonigen, Bui, Britt, Thomas, & Timko, 2016a; Samuel, LaPaglia, Maccarelli, Moore, & Ball, 2011). Further, personality characteristics are prominent risk factors for criminal recidivism in leading models of offender risk management. For example, in the Risk-Need-Responsivity model “antisocial personality pattern” is conceptualized as a dynamic (i.e., modifiable) risk factor and among the strongest predictors of recidivism (Andrews & Bonta, 2010). In prior studies with Veterans, this risk factor – operationalized in terms of symptoms or diagnoses of antisocial personality disorder – has only been examined in cross-sectional studies and been found to be more common among incarcerated than non-incarcerated veterans (Black et al., 2005; Shaw et al., 1987). Within the framework of normal-range personality theory, an antisocial personality pattern can be operationalized in terms of higher negative emotionality and lower constraint.

Recidivism among veterans 7 Importantly, these factors are viewed in the personality literature as dynamic constructs that can change via clinical interventions (Roberts et al., 2017). 1.4.

The present study The objectives of the present study were to (1) examine the prevalence of criminal recidivism

among veterans in SUD residential treatment who have a history of criminal justice involvement, and (2) identify predictors of recidivism following discharge from residential care. Secondary data analyses were conducted on a sample of veterans who were assessed at multiple points during an episode of residential SUD treatment and then followed for 12 months post-discharge. Based on our review of the literatures on SUD treatment and criminal justice involvement in veterans, we selected predictors a priori in the domains of (i) criminal history, (ii) functioning and SUD treatment processes, and (iii) personality. To inform potential interventions to reduce recidivism, we prioritized testing modifiable risk factors in these domains. In particular, we tested whether changes in normal-range personality factors add to the prediction of recidivism above and beyond established predictors in the other domains. We hypothesized that normal-range personality factors related to antisocial tendencies (i.e., low constraint and high negative emotionality; cf. Jones, Miller, & Lynam, 2011) would emerge as significant predictors of recidivism in this sample. 2. Material and Methods 2.1.

Setting The current study collected data from veterans of the US military who were enrolled in an SUD

residential treatment program at a VA Medical Center. Treatment programming was abstinence-based and included psychoeducation regarding substance use and mental health, relapse prevention, Cognitive Behavioral therapy, and Twelve-Step Facilitation. Program residents were involved in individual and group therapy approximately seven hours per day, five days per week. Program length

Recidivism among veterans 8 of stay was designed to be 3 to 6 months. Study participants were recruited from fliers and in-person program announcements, which invited program residents to participate in a study on personality and substance use. Residents who signed a recruitment sheet were contacted by a research assistant and informed about the study’s purpose, procedures, and risks and benefits (n=300). Thirteen residents declined to participate, and the remainder completed a phone screen. During the phone screen, residents who reported that they (i) were beginning a new episode of SUD residential treatment (i.e., no residential SUD care in the prior 3 months), and (ii) entered the treatment program to receive treatment for alcohol or drug use problems or endorsed at least one symptom of SUD in the 12 months prior to treatment entry were eligible for participation and scheduled for an in-person baseline assessment within the first week of program entry (n = 200). Residents were excluded from participation if they demonstrated cognitive impairment (i.e., not oriented to person, place, or time) on the Mini-Mental Status Examination (n=1) or reported that they received SUD residential care in the prior 3 months (n=86). A target sample size of 200 was sought based on a power analysis (alpha=.05, power=.80) to detect a medium effect size (d=0.4–0.5) of differences between personality traits and retention in SUD residential treatment. 2.2.

Sample For our secondary data analyses, we included only participants who reported a lifetime history

of criminal charges, convictions, or incarceration (n = 197; 98.5% of the total sample). Participants were mostly male (96.5%), White/Caucasian (47%) or African American (31%), and were not married at treatment entry (90%). On average, participants were 50.1 years old (SD = 9.0, Range = 25 to 77) with 13.4 years of education (SD = 1.9). Based on the Structured Clinical Interview for Axis I disorders (SCID; First, Spitzer, Gibbon, & Williams, 2002), 91% of participants met criteria for an SUD in the 12 months prior to treatment entry, and 42% met lifetime criteria for ASPD (45.2% of the sample met criteria for Childhood Conduct Disorder; 78.7% of the sample met criteria for Adult

Recidivism among veterans 9 Antisocial Behavior). The SCID has demonstrated good psychometric properties in SUD populations (Forman, Svikis, Montoya, & Blaine, 2009). In the 30 days prior to baseline, participants reported using alcohol 2.5 days, on average (SD = 4.9). Across various classes of drugs (i.e., opiates, barbiturates, cocaine, amphetamines, cannabis, hallucinogens, inhalants, and sedatives), the average number of days used in the past 30 ranged from 0 to 2.5 (SD range = 0–6.2). Of note, in the 30 days prior to the baseline assessment, many participants were in a controlled setting of some kind (e.g., inpatient unit; jail or prison) and therefore had limited access to alcohol and drugs. Regarding drugs used, 44%, 19%, and 15% of participants reported alcohol, cocaine, and amphetamines, respectively, as their major problems. Less than 6% reported heroin, other opioids, or cannabis as the major substance use problem. Based on the Life Events Checklist (Elhai, Gray, Kashdan, & Franklin, 2005), 87% of the sample reported a history of trauma exposure (33% reported combat-related trauma). Per medical records, 23% of the sample had a diagnosis of PTSD. 2.3.

Procedures During the baseline assessment, participants completed semi-structured interviews and self-

report questionnaires measuring personality, functioning, and history of criminal offending. One month post-baseline (n = 170, 86% retention) and at treatment discharge (n = 175, 89% retention), participants also completed self-report indices of treatment processes (e.g., resident relationship quality; treatment satisfaction). On average, participants stayed in treatment for 110 days (SD = 64.2). Participants were re-interviewed via phone at 6 and 12 months post-discharge to assess criminal justice involvement since leaving the residential treatment program (n = 151, 77% retention). Attrition analyses comparing veterans with and without data on criminal justice involvement during follow-up revealed no significant differences in age or any baseline criminal justice, functioning, or personality variables at p<.01. To determine if attrition was due in large part to participants being incarcerated during the follow-up period, we reviewed all notes from participants’ VA medical records in the 12

Recidivism among veterans 10 months after study enrollment. Out of the 46 participants who were lost to follow-up, only 5 had any mention in their medical records of being arrested or incarcerated during the follow-up period. We did not to include these five individuals in the outcome variable of recidivism, given that we were not able to systematically collect such data from all participants who were lost to follow-up. All interviews were conducted by research assistants with a Bachelor’s or Master’s degree in psychology who went through a week-long training with the first author to ensure fidelity to administration of the study measures. Participants were paid $40 for completing the baseline interview, $15 for completing the one-month interview, $25 for completing the discharge interview, and $50 each for the 6- and 12month post-discharge interviews. Data collection began in March of 2011 and ended in December of 2014. During the consent process, participants were informed that a Certificate of Confidentiality from the U.S. Department of Health & Human Services was obtained and would protect against any involuntary release of information about participants collected during the study, including their legal histories. All study procedures were approved by the local institutional review board. 2.4.

Measures

2.4.1. Criminal history and recidivism. Detailed information on participants’ history of criminal offending was collected via items from the Legal Section of the Addiction Severity Index (ASI), a structured clinical interview used in research studies of individuals in SUD treatment (McLellan et al., 1992). The ASI has demonstrated good reliability and validity in SUD residential populations (McLellan et al., 1992). Items from this section were used to create a dichotomous variable measuring whether participants at baseline had recent involvement in the criminal justice system (i.e., their admission to the residential program was prompted by the criminal justice system; they were on parole/probation; were awaiting charges, trial, or sentence when they entered the program; or they had been incarcerated in the 30 days prior to entering the treatment program). ASI items also inquired about the number of times participants were arrested and charged (regardless of conviction status) for

Recidivism among veterans 11 16 criminal offenses. We organized offenses into five categories (cf. LaSalle, 2011; Schultz et al., 2015): (i) violent offenses (rape, homicide, robbery, or assault charges); (ii) property offenses (burglary, larceny/breaking and entering, arson, shoplifting/vandalism, or forgery charges), (iii) public order offenses (weapons, prostitution, disorderly conduct, public intoxication/vagrancy, driving while intoxicated, contempt of court, or major driving offenses), (iv) parole/probation violations; and (v) drug charges. Information was also collected on number of criminal convictions and total number of months incarcerated in their lifetime. This information was collected from participants during the baseline interview to measure pretreatment offending history, and again at follow-up assessments at 6 and 12 months post discharge to measure criminal recidivism. At each follow-up assessment, participants were asked to report on criminal justice involvement since the last interview, not including any cases that were ongoing since treatment entry. Using all available post-discharge data, a dichotomous variable of criminal recidivism was constructed, which reflected any new charges, convictions, and/or periods of incarceration at any point during the 12-month follow-up period. 2.4.2. Functioning and SUD treatment processes 2.4.2.1. Substance use severity. We used the SCID (First et al., 2002) at baseline to collect information regarding participants' alcohol use disorder (AUD) and drug use disorder (DUD) symptoms in the 12 months prior to study enrollment – i.e., DSM-IV symptoms for abuse and dependence of alcohol and drugs. This approach is consistent with the dimensional approach to AUD and DUD diagnoses and severity in the DSM-5. Items assessing for AUD symptoms included, “In the past 12 months, did drinking cause you problems with other people (family, friends, co-workers) or getting into physical fights?” and “In the past 12 months, did you spend a lot of time drinking, being drunk or hung-over, or thinking about your next drink?” These items were modified to assess for DUD symptoms for the drug identified as primary by the participant – e.g., “In the past 12 months, did cocaine cause you problems with other people (family, friends, co-workers) or getting into physical

Recidivism among veterans 12 fights?” At baseline, participants endorsed an average of 4.97 AUD symptoms (SD = 3.87) and 4.91 DUD symptoms (SD = 3.96). 2.4.2.2. Psychiatric severity. The psychiatric composite score from the ASI was used to index severity of psychiatric distress at baseline. ASI composite scores are produced from sets of objective items that are internally consistent and are summed to create a standardized score that ranges from 0 to 1, with higher scores indicating greater problem severity. The SCID was also administered at baseline to assess symptoms of ASPD from DSM-IV; a symptom count variable was created from 14 items (yes/no) indexing Childhood Conduct Disorder (M = 3.00, SD = 3.07) and 7 items (yes/no) indexing Adult Antisocial Behavior (M = 4.09, SD = 1.74). The PTSD Checklist for Military Veterans (PCLM; Weathers, Huska, & Keane, 1991) was administered at baseline and treatment discharge to measure PTSD symptom severity. The PCL-M comprises 17 items, which correspond to the symptoms of PTSD from the DSM-IV. Respondents reported how much they had been bothered by each symptom, in response to stressful military experiences, in the past month, using a five-point Likert scale (1 = not at all; 5 = extremely). Item responses were summed to create a total score at baseline and discharge (both α = .96). 2.4.2.3. Social support. Subscales from the Life Stressors and Social Resources Inventory (LISRES; Moos & Moos, 1994) were administered at baseline to measure participants’ social support in the past month; items were rated on a five-point Likert scale (1 = never, 5 = often). Eleven items were combined into a composite of relationship quality, with higher scores indicating greater support from friends (α = .85). The 8-item Social Influence subscale measured the degree to which participants’ friends supported their efforts to quit alcohol and/or drugs (α = .64). 2.4.2.4. Resident relationship quality. At the 1-month and treatment discharge assessments, participants rated their relationship quality with other residents in the treatment program using the Stressors and Resources scales of the LISRES. At the 1-month assessment, a staff member from the

Recidivism among veterans 13 treatment program was also asked to rate participants on these two LISRES scales (α = .82). For each type of rating (self and staff), scores on the Stressors and Resources scales were combined into a composite of relationship quality, with higher scores indicating greater support from other residents in the program. For the self-ratings, scores were averaged across the 1-month (α = .78) and discharge (α = .75) assessments. 2.4.2.5. Treatment satisfaction and retention. At the 1-month and discharge assessments, participants’ satisfaction with the treatment program was indexed from the total score on an 11-item scale adapted from the Client Satisfaction Questionnaire (CSQ; Larsen, Attkisson, Hargreaves, & Nguyen, 1979). Items were rated on a 4-point scale and reflected whether the treatment program matched the participant’s treatment goals (e.g., To what extent did you and treatment staff agree on goals for your treatment?). Higher scores indicated greater satisfaction (α = .92 and .94 at 1-month and discharge, respectively). Treatment retention was operationalized as length of stay in the program (in days), which was determined from participants’ VA medical records. 2.4.3. Personality. The Brief Form of the Multidimensional Personality Questionnaire (MPQ; Patrick, Curtin, & Tellegen, 2002) is an omnibus measure of normal-range personality, which was administered at baseline and discharge. It consists of 155 items and measures individuals’ typical affective and behavioral styles using 11 primary scales, which form a higher-order, three-factor structure of Positive Emotionality (baseline α = .90; discharge α = .89), Negative Emotionality (baseline α = .89; discharge α = .90), and Constraint (baseline α = .77; discharge α = .78). Based on the recommended norms and cutoff criteria of the MPQ Brief From (Patrick et al., 2002), four participants’ protocols were flagged as invalid and excluded from the analyses. 2.5. Data Analysis Frequency distributions and descriptive statistics were calculated to describe participants’ history of criminal offending prior to treatment entry and during the 12-month follow-up period. Next,

Recidivism among veterans 14 a series of univariate logistic regressions were conducted to explore which variables across the domains of criminal history, functioning and SUD treatment processes, and personality were significant predictors of the dichotomous outcome of criminal recidivism (1 = yes) during the 12month follow-up. For variables which were available at both baseline and discharge (i.e., PTSD symptoms; personality), residualized change scores were obtained by regressing each variable measured at discharge on the baseline value and length of stay in treatment. The logistic regression models then included these residualized change scores as well as the baseline level as predictors of recidivism. Some treatment processes were measured one month into treatment and at treatment discharge (i.e., self-reported relationship quality with other residents; treatment satisfaction). To maximize use of the available data, scores on these variables that were included in the logistic regressions were based on either the 1-month or discharge assessment if only one time point was completed or the average of the scores across the two time points if both assessments were completed. Using this approach, data on treatment processes were available for 96% of the sample. Finally, the significant predictors from the univariate models were included in a multivariate logistic regression model as predictors of criminal recidivism. It should be noted that this approach reduces protection against experiment-wise error, but also reduces the risk of Type II errors. Given the dearth of literature on risk factors for recidivism among justice-involved veterans, we viewed our analyses as exploratory and therefore sought to protect against Type II errors. Risk for criminal behavior decreases linearly with age (Blonigen, 2010); therefore, age was included as a covariate in all regression models. All logistic regression models were fitted using full information maximum likelihood estimation in Mplus version 8.4 (Muthén & Muthén, 2019) so that results were based on all available information in the data and no participants were dropped from the analyses. Rates of missing data ranged from 0% to 35% across variables. Prior to the regression analyses, the data were checked for violations of assumptions (i.e., normality, multicollinearity). To address nonnormality of the continuous criminal

Recidivism among veterans 15 history variables (charges; convictions; total months incarcerated), these predictors were transformed using Winsorization by replacing values above the 95th percentile with the 95th percentile value in the regression analyses (Dixon, 1960). 3. Results Table 1 provides information on participants’ history of criminal justice involvement prior to entering the SUD residential treatment program. The vast majority of participants had a history of multiple criminal charges and convictions; for example, 94% of the sample reported two or more criminal charges in their lifetime, and nearly half of the sample reported 10 or more charges. Further, 78% of the sample reported two or more convictions in their lifetime, and nearly a quarter of the sample reported 10 or more convictions. Participants also reported a variety of criminal charges, with public order offenses most common (91% with at least 1 charge) and violent offenses least common (31% with at least 1 charge). On average, participants reported charges across 2.57 offense categories (SD = 1.33; Maximum = 5) and were incarcerated a total of 41.18 months (SD = 71.62; Maximum = 360) in their lifetime. A total of 105 participants (53%) reported recent involvement in the criminal justice system at the time of treatment entry. Regarding re-offending during the 12 months after discharge from the residential program, 33 participants (22%) recidivated during this period – i.e., had at least one charge, conviction, or period of incarceration. Among those who were recently involved in the criminal justice system at the time of treatment entry, 24 participants (30%) recidivated during this period. Of all incidents of reoffending during the follow-up period, 41%, 13%, 11%, 6%, and 5% were for public order, property, parole/probation violation, drug, and violent charges, respectively. Table 2 shows the results from univariate logistic regressions examining variables in the domains of criminal history, functioning and SUD treatment processes, and personality as predictors of

Recidivism among veterans 16 criminal recidivism during follow-up. Regarding criminal history, neither the number of convictions nor the total number of months incarcerated over the lifetime predicted criminal recidivism in the 12 months after discharge from treatment. However, those who had more total charges and were recently involved in the criminal justice system at treatment entry had significantly greater odds of recidivism during the follow-up period. In terms of functioning and SUD treatment processes, smaller reductions in PTSD symptoms during treatment, relative to the overall sample, were associated with a greater likelihood of recidivism during follow-up. Social support at treatment entry was also significantly linked with criminal recidivism after discharge. Specifically, greater support from friends – in terms of overall relationship quality and support for quitting substance use – predicted lower odds of recidivism during the follow-up period. Regarding resident relations, staff ratings of greater support from other residents during treatment were associated with lower odds of recidivism post-discharge. Regarding personality changes, smaller reductions in Negative Emotionality and smaller increases in Constraint during treatment, relative to the overall sample, were significant predictors of recidivism during the follow-up period. The significant predictors from the univariate logistic regressions were included in a multivariate logistic regression model (Table 3). Poorer relationship quality with friends at treatment entry and lower staff ratings of patients’ relationship quality with other residents during treatment were significantly associated with increased odds of recidivism in the 12 months after treatment discharge. After accounting for these factors as well as criminal history, smaller reductions in Negative Emotionality during treatment was significantly associated with increased odds of recidivism during the follow-up period. 4. Discussion

Recidivism among veterans 17 This study sought to expand the knowledge base of SUD treatment outcomes among military veterans by examining the prevalence and predictors of criminal recidivism in this population following discharge from residential treatment. An extensive and varied criminal history was the norm in this sample of veterans in SUD treatment. About one-half of the sample had recently been involved in the criminal justice system at treatment entry (that is, court-referred; on probation or parole; awaiting charges, trial, or sentence; and/or had been incarcerated in the 30 days prior to treatment entry). Within 12 months of discharge from the residential treatment program, 22% of patients recidivated, with a higher rate of 30% among those with recent criminal justice system involvement. Among the functioning and treatment process variables, various indicators of social support – both at treatment entry (i.e., friend relationship quality; social influences) and during treatment (i.e., relationship quality with other residents in the program) – and changes in PTSD severity during treatment were significant predictors of recidivism in the univariate models. After accounting for predictors in domains of criminal history and functioning/treatment processes, change in Negative Emotionality over the course of treatment was a significant predictor of recidivism post-discharge. 4.1.

Rate of criminal recidivism among veterans in SUD treatment: Implications for clinical practice Given the absence of prior estimates, the rate of criminal recidivism observed for the current

sample is an important contribution to the literature on veterans in SUD treatment. Putting these findings in context with prior literature, the 12-month recidivism rate in this SUD sample was higher than the rate reported in past research on justice-involved veterans over a similar timeframe. For example, in a national sample of Veteran Treatment Court participants, 14% were reincarcerated nearly one year after court entry (Tsai et al., 2018). By contrast, the recidivism rate in the present study was comparable to a study of non-veterans in SUD treatment programs in correctional settings, which

Recidivism among veterans 18 found that 26% returned to custody one year after release (Staton-Tindall, Harp, Winston, Webster, & Pangburn, 2015). Further, among US prisoners across 30 States, the rate of recidivism one year after release is 23–46% (Durose et al., 2014). Although the rate of recidivism in the current study (22%) was on the lower end of this range, it is important to note that only about one-half of our veteran sample was recently involved in the justice system. The fact that the rate of recidivism in this sample is comparable to the rate for adults who were released from a correctional setting highlights the prevalence of this issue among veterans in SUD treatment with a history of criminal justice involvement. By and large, studies examining the prevalence and predictors of recidivism have tended to focus either on individuals in corrections-based SUD treatment, or individuals supervised in the community on parole/probation or diverted from incarceration. The current study reinforces the conclusions of Weaver et al. (2013) that a history of involvement in the criminal justice system is the norm for most veterans in SUD treatment, not just those supervised in the community or mandated to treatment. Further, the rate of recidivism among Veterans in SUD treatment may be comparable to reentry or treatment court samples. Accordingly, it may behoove SUD treatment programs for veterans to routinely assess for patients’ criminal histories at treatment entry, monitor their recidivism risk over the course of treatment, and augment the curriculum of these programs with services directly focused on reducing risk for recidivism. For example, services that align with established models of offender rehabilitation such as the Risk-Need-Responsivity model or treatments that directly target risk factors for recidivism that are not typically the focus of SUD treatment (e.g., criminogenic thinking; Little & Robinson, 1988, 2013) may help tailor services to the large population of justice-involved veterans in SUD treatment programs in VHA. 4.2.

Implications for developing and testing interventions to reduce recidivism risk among veterans in SUD treatment

Recidivism among veterans 19 Another contribution of the current study was identifying predictors of recidivism among veterans following discharge from SUD residential treatment. Importantly, the prospective design of the current study afforded examination of whether change in certain factors over the course of residential care is related to risk for criminal recidivism post treatment. Two insights from the regression models merit discussion. First, many of the functional indices and process variables that are traditionally the focus of SUD treatment-outcome research (e.g., SUD and psychiatric severity; treatment satisfaction and retention) were not significant predictors of criminal recidivism. Consequently, assessments that are limited to more traditional domains of SUD treatment may be insufficient for estimating recidivism risk among veterans in these programs. In addition, combatrelated PTSD, which is common among veterans in SUD treatment (Seal et al., 2011), has been theorized to be a unique risk factor for criminal recidivism among justice-involved veterans (Blonigen et al., 2016b; in press). However, changes in PTSD were only significant in the univariate model. This is consistent with findings from the offender rehabilitation literature that trauma and PTSD – in the absence of other factors such as anger or irritability – are not robust predictors of criminal recidivism (Andrews & Bonta, 2010; Elbogen et al., 2012a). Interestingly, ASPD symptom count was not a significant predictor of recidivism. This may be due to the high rate of these symptoms in the current sample such that there was limited variance to account for differences in recidivism risk in the regression models. For example, nearly 8 in 10 participants met criteria for Adult Antisocial Behavior in their lifetime and endorsed an average of 4 (out of a possible 7) symptoms from the Adult criteria. Consequently, symptoms and diagnoses of DSM-defined ASPD may have limited value in predicting recidivism in samples with high rates of criminal justice involvement. Among the variables we tested from the domain of functioning/treatment processes, indicators of social support were the most consistent markers of recidivism risk. In particular, veterans who reported having more support from friends at the time of treatment entry were less likely to reoffend in

Recidivism among veterans 20 the 12 months after treatment discharge. The finding for relationship quality with other residents in treatment also aligns with the findings of social support at treatment entry. Collectively, the findings highlight how the interpersonal problems of veterans in SUD residential treatment may be a particularly salient marker of recidivism risk. Interpersonal problems are common among veterans in residential treatment and can adversely impact treatment outcomes (Harrison, Timko, & Blonigen, 2017). Incorporating treatments that focus directly on the development of healthy, prosocial interpersonal relationships (e.g., Network Support treatment; Litt, Kadden, Tennen, & KabelaCormier, 2016) or provide skills training in affective and interpersonal regulation (Cloitre, Koenen, Cohen, & Han, 2002) may help mitigate veterans risk of recidivism after residential care. A second insight was the potential role of personality in the prediction of criminal recidivism. To our knowledge, no prior studies have tested these factors as predictors of recidivism in SUD treatment samples, despite such factors predicting a range of other SUD treatment outcomes (Blonigen et al., 2016a; Harrison et al., 2017; Samuel et al., 2011). Notably, in the Risk-Need-Responsivity model of offender rehabilitation, personality characteristics are core risk factors of recidivism and viewed as modifiable constructs (Andrews & Bonta, 2010). Consistent with this, a meta-analysis by Roberts and colleagues (2017) concluded that most personality traits, particularly Negative Emotionality, exhibit positive change in response to a range of therapies, including time-limited episodes of treatment. The current findings align with this perspective in that patients with smaller reductions in Negative Emotionality during SUD treatment had an increased risk for recidivism after discharge. Negative Emotionality reflects tendencies towards stress reactivity, aggression, and blame externalization, and is conceptualized as a transdiagnostic risk factor for multiple mental health conditions including SUD (Widiger & Oltmanns, 2017). From the standpoint of recidivism risk management, there may be value in monitoring patients’ levels of Negative Emotionality over the course of SUD treatment, as well as tailoring interventions for those at highest risk of recidivism to

Recidivism among veterans 21 emphasize enhancement of emotion regulation. Barlow and colleagues’ (2011) Unified Protocol is one intervention that was developed for this purpose as it directly targets reductions in Negative Emotionality. In contrast to this trait, and contrary to our hypotheses, changes in Constraint were associated with recidivism risk post-discharge in the univariate model only. Low Constraint (i.e., high impulsivity, risk taking) is a known risk factor for antisocial behavior (Jones et al., 2011); however, the extent to which such tendencies change over the course of SUD treatment for justice-involved veterans may not reliably predict risk for reoffending after discharge. 4.3.

Limitations and Future Directions Several limitations to the current study deserve mention. First, our recidivism outcome was

based on self-report rather than official records. Consideration of this limitation should be balanced with consideration of (a) the relatively high response rate for our follow-up period (77%), (b) the fact that those who were retained during the follow-up period were similar to those who were lost to attrition on baseline characteristics, (c) few indications from participants’ medical records that loss to follow-up was due to incarceration, and (d) strong concordance between criminal history data collected via self-report and official records in prior research (Forrest, Edwards, & Vassallo, 2014). Nonetheless, future research on the current topic should aim to validate self-reports of reoffending with official records on rearrest and reincarceration. Further, time to rearrest or reincarceration after discharge was not collected on participants. Such information would also be of interest in future studies. Second, the study involved secondary data analysis and therefore was not powered to systematically identify a large number of predictors of recidivism. Further, the dataset did not include information on other variables that would have been useful to explore as predictors of recidivism (e.g., co-occurring psychiatric diagnoses; Wilton & Stewart, 2017). Nevertheless, our work sets the stage for future studies to replicate and expand our findings. For example, this study was unable to examine the predictive validity of the study variables in a comparison group of Veterans in SUD residential care

Recidivism among veterans 22 without a history of justice involvement; this should be a focus of future research to enhance the external validity of the findings. Third, our sample was limited to a single site of predominantly male veterans in an SUD residential treatment program. Further, the non-random sampling may also limit the external validity of the findings. Future research on the prevalence and prediction of recidivism among veterans in SUD treatment should be expanded to a wider range of sites, other treatment settings (e.g., outpatient), female veterans, and use random sampling methods. Fourth, personality measures such as the MPQ are susceptible to contamination of state affects due to psychiatric distress and ongoing substance use (Duncan-Jones, Fergusson, Ormel, & Horwood, 1990). Thus, some of the variation in patients’ personality ratings at baseline may reflect transient state effects due to recent substance abuse rather than enduring tendencies that existed prior to treatment. This possibility should be tempered with consideration of the fact that participants’ rate of substance use in the 30 days prior to treatment entry was minimal, suggesting that contamination via state effects was also minimal. Finally, notwithstanding our inclusion of staff ratings of relationship quality, most of the data was gathered via self-report. 5. Conclusions In a sample of veterans in SUD residential treatment, a history of repeated involvement in the criminal justice system was the norm. Twenty-two percent of veterans recidivated in the 12 months after discharge from the treatment program, and 30% of those who were recently involved in the criminal justice system at the time of treatment entry re-offended during this period. These rates were comparable to those observed in re-entry or treatment-court samples (Staton-Tindall et al., 2015; Tsai et al., 2018). In a multivariate logistic regression model, after accounting for significant predictors from domains of criminal history and functioning/treatment processes, smaller reductions in Negative Emotionality during treatment predicted increased odds of recidivism post-discharge. Accordingly,

Recidivism among veterans 23 there may be value in enhancing the programming of SUD residential treatment programs with services that directly target risk for criminal recidivism.

Recidivism among veterans 24 Conflict of Interest:

All authors declare that they have no conflicts of interest.

Author Statement All authors contributed to and have approved the final manuscript. Dr. Blonigen lead the design of the study and wrote the initial draft of the manuscript. Dr. Macia conducted the statistical analyses. Drs. Smelson and Timko assisted with the study design, conducted literature searches, and revised portions of the introduction and discussion sections after the initial draft.

Acknowledgments Dr. Blonigen was supported by a Career Development Award (CDA-2-008-10S) from VA Clinical Science Research & Development (awarded to Dr. Blonigen). Dr. Timko was supported by a Senior Research Career Scientist Award (RCS-00-001) from VA Health Services Research & Development. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Veterans Health Administration.

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Recidivism among veterans 33 Table 1. Pre-treatment offending history among veterans in substance use disorder residential treatment. n (%) 0

1

2–4

5–9

≥ 10

--

11 (5.6%)

37 (18.8%)

55 (27.9%)

94 (47.7%)

Violent offense charges

136 (69.0%)

29 (14.7%)

22 (11.2%)

5 (2.5%)

5 (2.5%)

Property offense charges

123 (62.4%)

21 (10.7%)

31 (15.7%)

12 (6.1%)

10 (5.1%)

17 (8.6%)

23 (11.7%)

63 (32.0%)

48 (24.4%)

46 (23.4%)

Drug charges

100 (50.8%)

37 (18.8%)

34 (17.3%)

14 (7.1%)

12 (6.1%)

Parole/probation violations

102 (51.8%)

29 (14.7%)

30 (15.2%)

16 (8.1%)

20 (10.2%)

Convictions

44 (22.3%)

28 (14.2%)

45 (22.8%)

34 (17.3%)

46 (23.4%)

Charges

Public order charges

Note. Pretreatment offending history reflects participants’ lifetime history of criminal justice involvement prior to entry into the substance use disorder residential treatment program. Violent offense charges = rape, homicide/manslaughter, robbery, and assault. Property offense charges = arson, shoplifting, forgery, and burglary/larceny/breaking and entering. Public order charges = weapons offense, prostitution, disorderly conduct, driving while intoxicated, contempt of court, and major driving violations.

Recidivism among veterans 34 Table 2. Univariate logistic regressions of criminal history, functioning and SUD treatment processes, and personality change in the prediction of criminal recidivism 12 months post-discharge from residential treatment. Criminal recidivism during follow-up Predictors

Est.

(SE)

OR a

(95% CI)

0.032 **

(0.012)

1.033

(1.008, 1.058)

Convictions

0.030

(0.023)

1.031

(0.985, 1.078)

Total months incarcerated

0.002

(0.003)

1.002

(0.996, 1.008)

0.993 *

(0.432)

2.699

(1.158, 6.294)

Alcohol use disorder symptom count

0.119

(0.066)

1.126

(0.989, 1.282)

Drug use disorder symptom count

0.012

(0.075)

1.012

(0.873, 1.173)

Psychiatric composite (ASI)

-0.133

(0.919)

1.142

(0.189, 6.897)

ASPD symptoms

0.028

(0.048)

1.028

(0.936, 1.129)

0.050 *

(0.021)

1.051

(1.009, 1.095)

Friend relationship quality

-0.623 *

(0.260)

1.866

(1.120, 3.106)

Social influences

-0.096 *

(0.044)

1.101

(1.011, 1.199)

Self-rating b

-0.353

(0.289)

1.425

(0.808, 2.506)

Staff-rating

-0.771 **

(0.293)

2.160

(1.218, 3.831)

CSQ total scores b

-0.016

(0.030)

1.016

(0.958, 1.079)

Length of stay (Days)

-0.002

(0.003)

1.002

(0.996, 1.008)

Positive Emotionality

-0.056

(0.039)

1.058

(0.979, 1.143)

Negative Emotionality

0.131 **

(0.043)

1.140

(1.049, 1.240)

Constraint

-0.067 *

(0.033)

1.070

(1.003, 1.142)

Criminal history Charges

Recent criminal justice involvement (1 = yes) Functioning and SUD treatment processes Substance use severity (SCID)

Psychiatric severity

PTSD symptoms change (PCL) Social support (LISRES)

Resident relationship quality (LISRES)

Treatment satisfaction and retention

Personality change (MPQ)

Recidivism among veterans 35 Note. Estimates for the model intercepts are not shown but were included in all models. Change = residualized changes scores estimated by regressing the variable measured at discharge on baseline values and length of stay in treatment. Age was included as a covariate in all regression models, and baseline level was included as a covariate for change variables. Recent criminal justice involvement = involvement in the criminal justice system prompting enrollment in the current treatment program; being on parole/probation or awaiting charges, trial, or sentence when they began treatment; or incarcerated in the 30 days prior to entering the treatment program. Est. = estimated coefficient; SE = standard error; OR = odds ratio; CI = confidence interval; PCL = PTSD Checklist; ASI = Addiction Severity Index; LISRES = Life Stressors and Social Resources Inventory; CSQ = Client Satisfaction Questionnaire; MPQ = Multidimensional Personality Questionnaire – Brief Form; SCID = Structured Clinical Interview for Axis I disorders. a For ease of interpretation, an inverse odds ratio and confidence interval is reported for predictors with a negative estimated coefficient. b Predictors reflect average of during treatment and discharge scores. * p < 0.05, ** p < 0.01.

Recidivism among veterans 36 Table 3. Multivariate logistic regression of significant criminal history, functioning and SUD treatment processes, and personality variables in the prediction of criminal recidivism 12 months post-discharge from residential treatment.

Predictors

Criminal recidivism during follow-up Est. (SE) OR a (95% CI)

Charges

0.022

(0.016)

1.022

(0.991, 1.054)

Recent criminal justice involvement (1 = yes)

0.987

(0.572)

2.684

(0.875, 8.234)

-0.839 *

(0.414)

2.315

(1.027, 5.208)

-0.020

(0.076)

1.020

(0.879, 1.185)

-1.013 **

(0.343)

2.755

(1.404, 5.405)

0.033

(0.029)

1.034

(0.976, 1.095)

Negative Emotionality change (MPQ)

0.123 *

(0.055)

1.131

(1.014, 1.261)

Constraint change (MPQ)

-0.036

(0.043)

1.036

(0.951, 1.129)

Friend relationship quality (LISRES) Social Influences (LISRES) Resident relationship quality (LISRES - staff-rating) PTSD symptom change (PCL)

Note. Estimates for the model intercepts are not shown but were included in all models. Predictors were those that were significant in the univariate models in Table 3. Age was included as a covariate. Change = residualized changes scores estimated by regressing the variable measured at discharge on baseline values and length of stay in treatment. Recent criminal justice involvement = involvement in the criminal justice system prompting enrollment in the current treatment program; being on parole/probation or awaiting charges, trial, or sentence when they began treatment; or incarcerated in the 30 days prior to entering the treatment program. Est. = estimated coefficient; SE = standard error; OR = odds ratio; CI = confidence interval; LISRES = Life Stressors and Social Resources Inventory; PCL = PTSD Checklist; MPQ = Multidimensional Personality Questionnaire – Brief Form. a For ease of interpretation, an inverse odds ratio and confidence interval is reported for predictors with a negative estimated coefficient. * p < 0.05, ** p < 0.01.

Recidivism among veterans 37 

An extensive and varied criminal history is the norm for veterans in SUD treatment.



Many veterans are rearrested or reincarcerated one year after discharge.



Personality changes predict recidivism above and beyond other known predictors.



Services focused on recidivism risk management may benefit veterans in treatment.