Repeated arrests and associated mental health characteristics in U.S. veterans: Results from a nationally representative study

Repeated arrests and associated mental health characteristics in U.S. veterans: Results from a nationally representative study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Repeated arrests and associated mental health characteristics in U.S. veterans: Results from a nationally representative study Anastasia Jankovsky, Avalon Moore, Elina Stefanovics, Jack Tsai, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7861353/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract INTRODUCTION: Military veterans experience higher rates of mental disorders compared to non-veterans, and criminal justice system involvement (CJI) may exacerbate these conditions. Despite this, few population-based studies have examined the prevalence, correlates, and mental health burden associated with CJI among veterans. METHODS: Data were analyzed from the National Health and Resilience in Veterans Study, a nationally representative survey of 4,069 U.S. veterans. Prevalence of CJI and its sociodemographic and clinical correlates were assessed, comparing veterans with no arrests, one arrest, and two or more arrests. RESULTS: Overall, 30.1% of veterans reported a history of CJI, including 16.7% with one arrest and 13.4% with two or more arrests. Veterans with multiple arrests were more likely to be male, from racial or ethnic minority groups, unmarried or unpartnered, of lower educational attainment and income, and to utilize VA healthcare. They also exhibited significantly higher rates of current drug use disorders, suicidal ideation, and suicide attempts compared with veterans with one or no arrests. CONCLUSION: CJI is common among U.S. veterans and is associated with substantial sociodemographic disadvantage and mental health burden. These findings underscore the need for targeted prevention and intervention efforts—particularly for veterans with repeated arrest histories—to address substance use, suicidality, and related psychosocial risk factors. veterans repeated arrests substance use suicide incarceration mental health Introduction Mental health disorders among U.S. military veterans remain a pressing public health concern, with elevated rates of conditions such as posttraumatic stress disorder (PTSD), depression, substance use disorders (SUDs), and suicidality relative to the general population (Inoue et al., 2023 ; Blodgett et al., 2015 ). A growing body of research highlights that individuals who have come into contact with the criminal justice system are of particular concern. Veterans are currently estimated to be twice as likely to face incarceration compared to their civilian counterparts (Yen, 2023 ), and rates of mental disorders are disproportionately higher in JIVs than in justice-involved civilians (Blodgett et al., 2015 ). Notably, approximately 62% of incarcerated veterans meet diagnostic criteria for at least one mental health disorder (Blodgett et al., 2015 ). These findings suggest that veterans who become entangled in the criminal justice system represent a high-risk group, with implications not only for individual well-being but also for public safety, healthcare resource allocation, and reintegration support needs (Edwards et al., 2023). Understanding the pathways that lead veterans to justice involvement (especially for individuals with repeated arrests) is an urgent priority for both clinical and policy-based interventions. Prior research has identified a variety of individual-level risk factors for justice involvement among veterans, including early life adversity, trauma exposure, untreated mental illness, and substance use disorders (Blodgett et al., 2015 ; Hoggatt et al., 2021 ). To our knowledge, most studies to date have used binary categorizations of criminal justice involvement (CJI), examining whether a veteran has ever been arrested or incarcerated. In other words, past studies are not considering the role of arrest frequency as a marker of more chronic or entrenched involvement in the system. Frequency of arrests and recidivism may indicate more severe psychosocial instability, unmet treatment needs, or structural disadvantages (Timko et al., 2020 ). Moreover, much of the existing literature relies on treatment-seeking or criminal justice samples, which may introduce selection bias and limit generalizability. To our knowledge, no population-based study has stratified justice involvement by arrest frequency (e.g., 0, 1, or 2 + lifetime arrests) to examine how rates of mental health problems and other risk factors vary across levels of contact with the justice system. This lack of stratification limits our understanding of whether recidivism reflects a distinct set of psychosocial vulnerabilities among veterans. Addressing this gap is particularly important given evidence that recidivism rates may be higher in veteran subgroups such as Black, Indigenous, and People of Color (BIPOC) males (Holloway et al., 2022 ), yet such disparities remain understudied in representative veteran samples. Several sociodemographic, trauma-related, and military service characteristics have been linked to criminal justice involvement among veterans and were therefore selected for inclusion in the present analysis. Past studies endorse veterans from marginalized racial and ethnic backgrounds, particularly Black and Hispanic men, to be disproportionately represented in CJI. In fact, BIPOC veterans are more likely to experience arrest and re-incarceration than White veterans (Holloway et al., 2022 ). Similarly, lower education has consistently been associated with higher rates of CJI in both civilian and juvenile populations alike (Siennick & Widdowson, 2022 ), though less is known about its relevance in older, community-dwelling veterans. In addition, income instability, unemployment, and lack of retirement status have also been shown to contribute to elevated risk of arrest and recidivism (Hoggatt et al., 2021 ). Most existing research on JIVs relies on treatment-seeking or incarcerated samples, limiting the generalizability to the broader veteran population (Tsai et al., 2016; Blonigen et al., 2020 ). Importantly, justice involvement is often examined as a binary outcome, failing to account for arrest frequency as a potential marker of more severe psychopathological risk. Given these current gaps in literature, the present study utilizes data from the National Health and Resilience in Veterans Study (NHRVS), a nationally representative survey of U.S. veterans. By categorizing arrest history into three groups (no arrests, one arrest, and two or more arrests) this study aims to shine a light on the impact of recidivism. Specifically, we aimed to (1) identify sociodemographic, military, and trauma-related characteristics associated with arrest frequency; and (2) examine whether arrest frequency is independently associated with current mental health problems, including substance use and suicidality. Findings from this research can inform early identification and tailored interventions for at-risk veterans across both VA and non-VA healthcare settings. Methods Data were analyzed from the National Health and Resilience in Veterans Study (Fogle et al., 2020 ; Wisco et al., 2022 ), which surveyed a nationally representative sample of 4,069 US veterans. The NHRVS sample was ascertained from KnowledgePanel®, a research panel maintained by the survey research firm Ipsos, which consists of more than 50,000 households. KnowledgePanel® is an online, probability-based, non-volunteer access survey panel of U.S. adults that covers approximately 98% of U.S. households. Participants completed a 50-minute online survey between 11/18/19 and 3/8/20 (median completion date: 11/21/19). Participants were recruited through national random sampling via telephone and postal mail. Internet and computer access were provided if needed. Post-stratification weights based on demographic distributions of US veterans from concurrent US Census data were applied in inferential analyses. This raking adjustment of weights, which was conducted by the Ipsos statistical team, used benchmark distributions of U.S. military veterans from the most contemporaneous August 2019 Current Veteran Population Supplemental Survey and included gender, age, race/ethnicity, Census region, metropolitan status, education, household income, branch of military service, and years of military service. All participants provided electronic informed consent prior to participation in the study. The study protocol was reviewed and approved by the Institutional Review Board of the VA Connecticut Healthcare System. All procedures were conducted in accordance with the Declaration of Helsinki and relevant regulatory standards. Sample Participants were recruited through national random sampling using telephone and postal mail outreach. Participants were eligible if they were U.S. military veterans aged 18 or older. Internet and computer access were provided to participants if requested. Post-stratification weights based on demographic distributions of participants from U.S. Census data were applied to enhance generalizability to broader veteran populations. The current sample was drawn from KnowledgePanel®, a probability-based, non-volunteer access survey panel maintained by Ipsos. KnowledgePanel® includes over 50,000 households and covers approximately 98% of U.S. households. Measures The NHRVS survey assessed a wide range of demographics, including sociodemographic characteristics, military history, age, gender, race/ethnicity, education, marital status, employment status, and household income. A detailed list of all measures used is presented in Supplemental Table 1. Arrest History Arrest history was assessed with the question: “In your lifetime, were you ever arrested?” Veterans who responded “yes” were asked a follow-up question: “How many times?” Responses were re-coded into a three-level variable representing 0, 1, or 2 + lifetime arrests. Participants who endorsed any history of arrest were also asked to identify the nature of the offense(s) they were arrested for. Sociodemographic and Military Variables The following sociodemographic variables were assessed: age, gender, race/ethnicity, marital status, education, employment/retirement status, household income, and whether the veteran used the VA as their primary source of healthcare. Military service characteristics included era of service and exposure to combat. Trauma and Psychopathology Trauma exposure was assessed via cumulative count of Adverse Childhood Experiences (ACEs) and lifetime trauma events. Specific trauma and ACES were used, as seen in Supplemental Table 1. Psychiatric variables included lifetime and current diagnoses of major depressive disorder, direct and indirect traumas, military sexual traumas, posttraumatic stress disorder, alcohol and substance use disorders, suicide attempts and ideation, anxiety disorders, and gambling disorder. Data Analyses Item-level missing data (< 5%) were imputed using chained equations. First, analyses of variance and chi-square tests were conducted to compare characteristics between veterans with no, 1, and 2 + arrests. Second, a multinomial logistic regression analysis was conducted to identify sociodemographic, military, and trauma-related variables that differentiated veterans with 1 and 2 + arrests; two sets of analyses with different reference categories were conducted to enable comparison of all groups (i.e., 1 and 2 + vs. 0 arrests; 2 + arrests vs. 1 arrest) Third, a series of binary multivariable logistic regression analyses were conducted to examine association between arrest history and psychiatric variables. Sociodemographic, military, and trauma-related variables were adjusted in these analyses; analyses of suicidality variables were additionally adjusted for lifetime psychiatric and substance use disorders. Results The overall sample was 90.2% male, with 78.1% identifying as White, 11.2% as Black, 6.6% as Hispanic, and 4.2% as another race or multiracial, and had a mean age of 62.2 years (SD = 15.7). Nearly one-third (32.7%) had obtained a college degree or higher education, 72.4% were married or living with a partner, and 58.5% reported a household income above $ 60,000. Nearly half of the sample (44.3%) was retired. Of all respondents, 49.7% served in the Army, 21.2% in the Navy, 19.6% in the Air Force, and 6.5% in the Marine Corps. Slightly more than one-third of veterans reported having been exposed to combat (35.0%). A total of 2,939 (69.9%) of veterans denied ever having been arrested, and 642 (16.7%) and 468 (13.4%) reported 1 or 2+ (mean = 3.5, SD = 3.1, range = 2–32) arrests, respectively. Veterans with 2 + arrests were more likely than those with 1 arrest to have been incarcerated (14.3% vs. 8.2%, χ2(1) = 11.59, p < .001) and to report substance-related offenses (43.5% vs. 33.5%) and less likely to report criminal-mischief-related offenses (2.2% vs. 5.7%), χ2(8) = 20.85, p < .001. Table 1 (top) shows results of analyses examining sociodemographic, military, and trauma-related correlates of arrest history. A multivariable analysis revealed that, relative to veterans with no arrest history, those with 1 or 2 + arrests were younger and more likely to have lower education and not be retired; they also reported more adverse childhood experiences (ACEs) and traumas. Veterans with 2 + arrests were additionally more likely to be male, Black, and unmarried/partnered, to have lower household incomes, and to use the VA as their primary source of healthcare. Relative to veterans with 1 arrest, those with 2 + arrests were more likely to be male, non-White, and unmarried/partnered, to have lower education and household incomes, and to use the VA as their primary source of healthcare. Table 1 Correlates of arrest history among U.S. military veterans Sociodemographic, Military, and Trauma-Related Correlates x No Arrests N = 2,939 (weighted 69.9%) 1 Arrest N = 642 (weighted 16.7%) 2+ Arrests N = 468 (weighted 13.4%) 1 Arrest vs. None 2 + Arrests vs. None 2 + Arrests vs. 1 Arrest N (weighted %) or Weighted mean (SD) N (weighted %) or Weighted mean (SD) N (weighted %) or Weighted mean (SD) χ 2 or F P value Adjusted OR (95%CI) Adjusted OR (95%CI) Adjusted OR (95%CI) Age (in years) 63.0 (15.7) 60.1 (16.2) 60.3 (14.8) 13.67 < .001 0.98 (0.98–0.99)** 0.98 (0.98–0.99)** 1.00 (0.98–1.01) Male sex (vs female) 2526 (89.1%) 577 (90.7%) 443 (95.1%) 18.72 0.008 1.19 (0.80–1.76) 2.38 (1.34–4.21)** 1.96 (1.03–3.87)* White (vs non-White) 2459 (80.1%) 512 (78.2%) 333 (67.4%) 42.82 < .001 0.89 (0.66–1.94) 0.51 (0.38–0.69)*** 0.58 (0.40–0.84)** Black (vs non-Black) 188 (10.2%) 53 (10.9%) 53 (16.1%) 16.01 0.034 1.07 (0.71–1.62) 1.69 (1.13–2.51)* 1.57 (0.94–2.62) Married/partnered 2141 (75.1%) 451 (74.6%) 281 (56.1%) 84.46 < .001 0.97 (0.76–1.25) 0.42(0.33–0.55)*** 0.43 (0.31–0.60)*** College degree or higher 1429 (36.4%) 266 (30.3%) 122 (16.3%) 85.89 < .001 0.76 (0.60–0.96)* 0.34 (0.25–0.45)*** 0.45 (0.32–0.62) *** Retired 1656 (46.9%) 343 (39.9%) 217 (35.8%) 29.34 $ 60,000 1783 (61.9%) 372 (61.8%) 194 (37.6%) 113.58 < .001 0.99 (0.79–1.26) 0.37 (0.29–0.48)*** 0.37 (0.27–0.51)*** Combat veteran 946 (34.0%) 241 (39.4%) 160 (34.9%) 6.93 0.17 1.26 (0.99–1.60) 1.04 (0.79–1.36) 0.82 (0.59–1.15) VA primary healthcare 496 (18.6%) 141 (21.4%) 148 (29.9%) 36.32 < .001 1.19 (0.90–1.58) 1.87 (1.41–2.48)*** 1.57 (1.10–2.24)** Adverse childhood experiences 1.3 (1.7) 2.0 (2.2) 2.2 (2.4) 87.78 < .001 1.26 (1.19–1.34)*** 1.21 (1.14–1.28)*** 1.04 (0.97–1.12) Total traumas 8.3 (7.9) 10.7 (9.3) 10.0 (10.1) 26.27 < .001 1.02 (1.01–1.04)** 1.03 (1.02–1.04)*** 0.99 (0.97–1.01) Note . OR = odds ratio; 95%CI = 95% confidence interval. Table 2 Association between arrest frequency and psychiatric variables among U.S. military veterans No Arrest N = 2,939 (weighted 69.9%) 1 Arrest N = 642 (weighted 16.7%) 2+ Arrests N = 468 (weighted 13.4%) 1 Arrest vs. None 2 + Arrests vs. None 2 + Arrests vs. 1 Arrest N (weighted %) N (weighted %) N (weighted %) χ 2 P value Adjusted OR (95%CI) Adjusted OR (95%CI) Adjusted OR (95%CI) Major depressive disorder 174 (7.8%) 62 (9.8%) 55 (12.5%) 13.77 .001 0.89 (0.65–1.21) 1.02 (0.74–1.40) 1.20 (0.81–1.77) Posttraumatic stress disorder 134 (6.0%) 43 (6.7%) 42 (9.8%) 10.64 .005 0.72 (0.49–1.04) 1.20 (0.83–1.73) 1.47 (0.92–2.35) Generalized anxiety disorder 147 (6.7%) 44 (9.5%) 40 (12.0%) 20.05 < .001 1.01 (0.73–1.40) 1.23 (0.88–1.72) 1.18 (0.78–1.76) Alcohol use disorder 191 (7.7%) 86 (15.7%) 83 (18.6%) 80.64 < .001 1.92 (1.48–2.48)*** 2.36 (1.80–3.10)*** 1.25 (0.92–1.71) Drug use disorders 158 (6.5%) 67 (10.5%) 89 (24.2%) 160.45 < .001 1.42 (1.05–1.93)* 3.10 (2.35–4.09)*** 2.34 (1.66–3.29)*** Problem gambling 111 (3.9%) 31 (7.3%) 30 (7.4%) 21.78 < .001 1.59 (1.11–2.27)* 1.51 (1.02–2.23)* 0.90 (0.56–1.44) Suicide attempt 74 (3.3%) 26 (4.0%) 36 (6.8%) 14.56 < .001 0.74 (0.46–1.18) 1.14 (0.74–1.77) 1.93 (1.10–3.39)* Suicidal ideation 200 (7.8%) 54 (9.9%) 58 (14.5%) 25.93 < .001 0.88 (0.65–1.20) 1.08 (0.80–1.46) 1.54 (1.06–2.24)* Suicidal intent 30 (1.0%) 8 (1.8%) 11 (2.9%) 12.92 .002 1.02 (0.51–2.04) 1.49 (0.79–2.82) 1.52 (0.70–3.28) Note . OR = odds ratio; 95%CI = 95% confidence interval. Odds ratios for analyses of psychiatric correlates are adjusted for age, sex, race/ethnicity, marital status, education, retirement status, household income, combat veteran status, primary source of healthcare, adverse childhood experiences, and cumulative trauma histories. Analyses of suicide-related variables are additionally adjusted for lifetime major depressive, posttraumatic stress, and alcohol, drug, and nicotine use disorders. Table 1 (bottom) shows results of analyses examining psychiatric variables by arrest history. Multivariable analyses revealed that, relative to veterans with no arrest history, those with 1 or 2 + arrests were more likely to screen positive for alcohol and drug use disorders and problem gambling. Further, relative to veterans with 1 arrest, those with 2 + arrests were more likely to screen positive for drug use disorders and to report a suicide attempt and current suicidal ideation. Discussion To our knowledge, this is the first study to examine the prevalence and correlates of arrest frequency in a contemporary, nationally representative sample of veterans. Results extend to the U.S. veteran population prior work documenting an association between frequency of recidivism and mental illness (Dalbir et al., 2022 ). The current study further extends this work to identify sociodemographic factors linked to arrest frequency in this population. While past research (Holloway et al., 2022 ) has suggested that Black, indigenous, and other people of color (BIPOC) males are more likely to be repeatedly arrested than their White counterparts, this is the first study to observe these findings in a veteran sample, as well as compare single and multiple arrests as separate variables. To our knowledge, extant studies that assess the relationship between education and recidivism rates have been conducted exclusively within juvenile populations (Fox et al., 2021 ; Azad & Ginner Hau, 2020; Robertson et al., 2020 ). Results of the present study extend this work to suggest that lower levels of education are associated with repeated arrests among veterans and could be a target of intervention for this population. Prior research suggests strong connections between history of arrests and mental and substance use disorders in JIVs (Blodgett et al., 2015 , Hoggatt et al., 2021 , Inoue et al., 2023 ). Further, Edwards and colleagues ( 2022 ) found that 47% of veterans at risk for suicide had a history of criminal arrests and co-occurring substance use disorders. After adjusting for sociodemographic, military, trauma, and lifetime psychiatric variables, veterans with 2 + arrests had 2-fold greater odds of screening positive for current drug use disorders and having attempted suicide, and approximately 1-in-7 had recently contemplated suicide. While prior work has linked recidivism to suicide in adolescents (Mallet et al., 2013), the current study extends this finding to repeated arrests in military veterans. Collectively, these results underscore the importance of history of repeated arrests as a potential risk factor for adverse mental health outcomes in veterans and suggest considering legal concerns as part of suicide risk assessments in this population. Links with problem gambling may be particularly relevant given changes in sports and internet gambling (Grubbs & Kraus, 2023 ). Regarding treatment, the Collaborative Chronic Care Model (CCM; Kim et al., 2019 ) may help minimize the adverse effects of substance use disorders post-incarceration in veterans, and mental health court programs may help mitigate risk of repeated arrests (Fox et al., 2021 ). Various local criminal justice partners and stakeholders working with the VA’s Veterans Justice Programs to connect eligible justice-involved veterans with VA healthcare and social services may also be beneficial (Tsai et al., 2017 ). These findings also suggest that targeted interventions may be most impactful if implemented prior to or immediately following a first arrest, ideally within VA or community mental health systems. Primary care providers, VA case managers, and community outreach workers are well-positioned to identify at-risk veterans and initiate early intervention. Given that most veterans are not engaged in VA health care services (Kline et al., 2022), further work is needed to implement and evaluate the effectiveness of training programs, resource sharing, collaborative care models, referral pathways, and veteran-centered care initiatives for veterans outside the VA system. While both veterans and general civilians share similar rates of incarceration (~ 33% of American civilians and ~ 30% of veterans endorsed CJI), mental disorders are more frequent in veterans compared to their justice-involved adult counterparts (Blodgett et al., 2015 ). Among treatment-seeking veterans, previous criminal history is relatively common, with Blonigen and colleagues ( 2020 ) reporting that 94% of veterans in their treatment program had past CJI, but no known studies have investigated the association between repeated arrests and mental health in community-dwelling veterans. Of note, our data suggest that veterans with a history of repeated arrests are more likely to seek help through the VA. The VA offers short-term outpatient counseling, intensive outpatient treatment, marital and family counseling, self-help (e.g., 12-step) groups, residential care, and relapse prevention (U.S. Department of Veterans Affairs, 2023). Community, peer, and family support often contribute importantly to reintegration for previously incarcerated veterans suffering from substance use disorders, and if implemented efficiently, may prevent future incarcerations and promote long-lasting recovery (Blonigen, 2020). Regarding other intervention strategies, studies have shown the importance of social support and connectedness when transitioning back into civilian society to decrease likelihood of repeat arrests and enhance recovery from substance use disorders among veterans (Blonigen et al., 2020 & Hoopsick et al., 2019). While models such as the Collaborative Chronic Care Model (CCM; Kim et al., 2019 ) have been implemented to minimize substance use disorders, research also suggests that mental health court programs correspond to a moderate reduction in for adults (Fox et al., 2021 ) and could be impactful for veteran populations. As emphasized by Tsai and colleagues ( 2023 ), CJI among veterans is of national concern, and continued research is needed to understand the epidemiology, treatment and services, role of justice, community, and healthcare systems and their interface, methodology and research resources, and policies related to CJI in this population. Additionally, veterans treatment courts (i.e., specialized court diversion programs tailored to justice-involved veterans) may be uniquely suited to address the co-occurring legal and behavioral health needs of this population and warrant further evaluation (Fox et al., 2021 ). Limitations While the present study is limited by the cross-sectional design, predominantly older, male, and White, non-Hispanic sample, and use of self-report measures, these limitations are counterbalanced by the large, nationally representative dataset, which yielded results that generalize to the U.S. veteran population. However, it is worth noting that the average age of participants in the sample was relatively high, which may limit generalizability to younger veterans or those who have more recently separated from service. Additionally, given the cross-sectional design of this study, we cannot determine the directionality or causality of associations between arrest frequency and mental health outcomes. Future Directions We recommend future research to collect more detailed data on veterans’ criminal justice involvement, including the specific nature, timing, and context of arrests. Grouping veterans with two or more arrests together may obscure important differences in risk factors and mental health outcomes between those with relatively few versus frequent arrests. A more nuanced understanding of criminal history patterns could inform tailored interventions targeted to distinct subgroups of justice-involved veterans. Additionally, longitudinal studies tracking changes over time could help clarify causal pathways between repeated arrests and mental health trajectories. Results highlight the importance of repeated arrests in relation to behavioral health concerns in veterans. However, further research is needed to examine the generalizability of results to higher-risk veterans; identify mechanisms underlying links between CJI and adverse mental health outcomes; and evaluate effectiveness of intervention strategies to help mitigate CJI and repeated arrests in this and other at-risk populations. Declarations Author Contribution A.J. and A.M. wrote main manuscript. A.J. conceptualized manuscript topic and organized meetings, writing, and coordination between authorsR.B. and M.P. supervised all work and provided data set, as well as mentorship to A.J. and A.M. R.B. and M.P. revised and supervised data analyses and methods sections . J.S. provided information and education on this specific population, as well as aiding in editing paper.E.S. contributed to data analyses. A.J. and E.S. prepared all tables. References Azad, A., & Ginner Hau, H. (2020, April). Adolescent females with limited delinquency: A follow-up on educational attainment and recidivism. In Child & Youth Care Forum (Vol. 49, pp. 325-342). Springer US. Blodgett, J. C., Avoundjian, T., Finlay, et al. (2015). Prevalence of Mental Health Disorders Among Justice-Involved Veterans. Epidemiologic Reviews, 37(1), 163–176. https://doi.org/10.1093/epirev/mxu003 Blonigen, D. M., Macia, K. S., Smelson, D., et al. (2020). Criminal recidivism among justice-involved veterans following substance use disorder residential treatment. Addictive Behaviors, 106. Dalbir, N., Wright, E. M., & Steiner, B. (2022). Mental Illness, Substance Use, and Co-Occurring Disorders among Jail Inmates: Prevalence, Recidivism, and Gender Differences. Corrections, 1–23. https://doi.org/10.1080/23774657.2022.2090028 Deahl, M. P., Klein, S., & Alexander, D. A. (2011). The costs of conflict: Meeting the mental health needs of serving personnel and service veterans. International Review of Psychiatry, 23(2), 201–209. https://doi.org/10.3109/09540261.2011.557059 Edwards, E. R., Epshteyn, G., Connelly, B., Redden, C., Moussa, C. E. H., Blonigen, D. M., ... & Osterberg, T. (2024). Understanding criminogenic risk factors among United States military veterans: An updated literature review. Criminal Justice Review, 49(4), 495-518. https://doi.org/10.1177/07340168231160862 Edwards, E. R., Gromatsky, M., Sissoko, D. R. G., et al. (2022). Arrest history and psychopathology among veterans at risk for suicide. Psychological Services, 19(1), 146–156. https://doi.org/10.1037/ser0000454 Fogle, B. M., Tsai, J., Mota, N., et al. (2020). The National Health and Resilience in Veterans Study: A Narrative Review and Future Directions. Frontiers in Psychiatry, 11. https://doi.org/10.3389/fpsyt.2020.538218 Fox, B., Miley, L. N., Kortright, K. E., et al. (2021). Assessing the Effect of Mental Health Courts on Adult and Juvenile Recidivism: A Meta-Analysis. American Journal of Criminal Justice, 46(4), 644–664. https://doi.org/10.1007/s12103-021-09629-6 Grubbs, J. B., & Kraus, S. W. (2023). The relative risks of different forms of sports betting in a U.S. sample: A brief report. Comprehensive Psychiatry, 127. https://doi.org/10.1016/j.comppsych.2023.152420 Hoggatt, K. J., Harris, A. H., Washington, D. L., et al. (2021). Prevalence of substance use and substance-related disorders among US Veterans Health Administration patients. Drug and Alcohol Dependence, 225. Holloway, E. D., Folk, J. B., Ordorica, C., et al. (2022). Peer, substance use, and race-related factors associated with recidivism among first-time justice-involved youth. Law and Human Behavior, 46(2), 140–153. https://doi.org/10.1037/lhb0000471 Hoopsick, R. A., Homish, D. L., Lawson, S. C., et al. (2022). Drug use over time among never‐deployed US Army Reserve and National Guard soldiers: The longitudinal effects of non‐deployment emotions and sex. Stress and Health, 38(5), 1045–1057. https://doi.org/10.1002/smi.3156 Inoue, C., Shawler, E., Jordan, C. H., et al. (2023). Veteran and Military Mental Health Issues. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK572092/ Kline, A. C., Panza, K. E., Nichter, B., Tsai, J., Harpaz-Rotem, I., Norman, S. B., & Pietrzak, R. H. (2022). Mental Health Care Use Among U.S. Military Veterans: Results From the 2019-2020 National Health and Resilience in Veterans Study. Psychiatric Services (Washington, D.C.), 73(6), 628–635. https://doi.org/10.1176/appi.ps.202100112 Kim, B., Bolton, R. E., Hyde, J., et al. (2019). Coordinating across correctional, community, and VA systems: applying the Collaborative Chronic Care Model to post-incarceration healthcare and reentry support for veterans with mental health and substance use disorders. Health & Justice, 7, 1-12. Mallett, C. A., Fukushima, M., Stoddard-Dare, P., et al. (2013). Factors related to recidivism for youthful offenders. Criminal Justice Studies, 26(1), 84–98. https://doi.org/10.1080/1478601X.2012.705539 Robertson, A.A., Fang, Z., Weiland, D., et al. (2020). Recidivism among justice-involved youth: Findings from JJ-TRIALS. Criminal justice and behavior, 47(9), pp.1059-1078. Siennick, S.E., Widdowson, A.O. Juvenile Arrest and Later Economic Attainment: Strength and Mechanisms of the Relationship. J Quant Criminol 38, 23–50 (2022). https://doi.org/10.1007/s10940-020-09482-6 Timko, C., Nash, A., Owens, M. D., Taylor, E., & Finlay, A. K. (2020). Systematic review of criminal and legal involvement after substance use and mental health treatment among veterans: Building toward needed research. Substance Abuse: Research and Treatment, 14. https://doi.org/10.1177/117822181990128 Tsai, J., Flatley, B., Kasprow, W. J., et al. (2017). Diversion of Veterans With Criminal Justice Involvement to Treatment Courts: Participant Characteristics and Outcomes. Psychiatric Services, 68(4), 375–383. https://doi.org/10.1176/appi.ps.201600233 Tsai, J., Kelton, K., Blonigen, D. M., et al. (2023). A Research Agenda for Criminal Justice Involvement Among U.S. Veterans. Military Medicine, usad201. https://doi.org/10.1093/milmed/usad201 U.S. Department of Veterans Affair. (2023). VA.gov | Veterans Affairs [Program Homepage]. https://www.ptsd.va.gov/ Wisco, B. E., Nomamiukor, F. O., Marx, B. P., Et al. (2022). H. Posttraumatic Stress Disorder in US Military Veterans: Results From the 2019-2020 National Health and Resilience in Veterans Study. Journal of Clinical Psychiatry, 83(2), 20m14029. https://doi.org/ 10.4088/JCP.20m14029 Yen, R. (2023). From Service to Sentencing: Unraveling Risk Factors for Criminal Justice Involvement Among U.S. Veterans. Council on Criminal Justice. Additional Declarations No competing interests reported. Supplementary Files SupplementalTable1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7861353","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535130221,"identity":"c3083059-cfa1-485a-8fe5-dbfbc486daf6","order_by":0,"name":"Anastasia Jankovsky","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYFCCAwyMDUCKH8QEsw4Qq0WygXgtDBAtBgdgLEJa+BsPP3s4o+KwvfHx7sQDP3cwyPHdSMCvReLAMXPDDWcOJ247c3bDwd4zDMaShLQwHDhgJvmwLS3B7EbuhsOMbQyJGwhpkT9w/BtIi73x/LdgLfUEtRgcOGMmubHNhnGDBC9YS4IBIS2GB86USc44Y5M440wu0C9tEoYzzzzAr0XuxvFtkj0VEvb87Wc3f/jZZiPPd5yALcAgQ+USUA4C/A1EKBoFo2AUjIKRDQDUqFNlju1+iQAAAABJRU5ErkJggg==","orcid":"","institution":"Yale University","correspondingAuthor":true,"prefix":"","firstName":"Anastasia","middleName":"","lastName":"Jankovsky","suffix":""},{"id":535130222,"identity":"c58c7337-d880-4e33-ba67-f2d61632cd8a","order_by":1,"name":"Avalon Moore","email":"","orcid":"","institution":"Yale University","correspondingAuthor":false,"prefix":"","firstName":"Avalon","middleName":"","lastName":"Moore","suffix":""},{"id":535130223,"identity":"7bc7f65d-59dc-42ac-bf85-5d8e5d0c3a34","order_by":2,"name":"Elina 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07:17:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":659262,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7861353/v1/546ffff0-773b-4500-8e27-fb05c805dccf.pdf"},{"id":94907354,"identity":"cabf6b1b-f457-473f-81a6-847d0398752e","added_by":"auto","created_at":"2025-11-01 07:46:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17582,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7861353/v1/22a740e81edd3a2c00baf63e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eRepeated arrests and associated mental health characteristics in U.S. veterans: Results from a nationally representative study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMental health disorders among U.S. military veterans remain a pressing public health concern, with elevated rates of conditions such as posttraumatic stress disorder (PTSD), depression, substance use disorders (SUDs), and suicidality relative to the general population (Inoue et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Blodgett et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A growing body of research highlights that individuals who have come into contact with the criminal justice system are of particular concern. Veterans are currently estimated to be twice as likely to face incarceration compared to their civilian counterparts (Yen, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and rates of mental disorders are disproportionately higher in JIVs than in justice-involved civilians (Blodgett et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Notably, approximately 62% of incarcerated veterans meet diagnostic criteria for at least one mental health disorder (Blodgett et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These findings suggest that veterans who become entangled in the criminal justice system represent a high-risk group, with implications not only for individual well-being but also for public safety, healthcare resource allocation, and reintegration support needs (Edwards et al., 2023). Understanding the pathways that lead veterans to justice involvement (especially for individuals with repeated arrests) is an urgent priority for both clinical and policy-based interventions.\u003c/p\u003e\u003cp\u003ePrior research has identified a variety of individual-level risk factors for justice involvement among veterans, including early life adversity, trauma exposure, untreated mental illness, and substance use disorders (Blodgett et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hoggatt et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To our knowledge, most studies to date have used binary categorizations of criminal justice involvement (CJI), examining whether a veteran has ever been arrested or incarcerated. In other words, past studies are not considering the role of arrest frequency as a marker of more chronic or entrenched involvement in the system. Frequency of arrests and recidivism may indicate more severe psychosocial instability, unmet treatment needs, or structural disadvantages (Timko et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, much of the existing literature relies on treatment-seeking or criminal justice samples, which may introduce selection bias and limit generalizability. To our knowledge, no population-based study has stratified justice involvement by arrest frequency (e.g., 0, 1, or 2\u0026thinsp;+\u0026thinsp;lifetime arrests) to examine how rates of mental health problems and other risk factors vary across levels of contact with the justice system. This lack of stratification limits our understanding of whether recidivism reflects a distinct set of psychosocial vulnerabilities among veterans. Addressing this gap is particularly important given evidence that recidivism rates may be higher in veteran subgroups such as Black, Indigenous, and People of Color (BIPOC) males (Holloway et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), yet such disparities remain understudied in representative veteran samples.\u003c/p\u003e\u003cp\u003eSeveral sociodemographic, trauma-related, and military service characteristics have been linked to criminal justice involvement among veterans and were therefore selected for inclusion in the present analysis. Past studies endorse veterans from marginalized racial and ethnic backgrounds, particularly Black and Hispanic men, to be disproportionately represented in CJI. In fact, BIPOC veterans are more likely to experience arrest and re-incarceration than White veterans (Holloway et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, lower education has consistently been associated with higher rates of CJI in both civilian and juvenile populations alike (Siennick \u0026amp; Widdowson, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), though less is known about its relevance in older, community-dwelling veterans. In addition, income instability, unemployment, and lack of retirement status have also been shown to contribute to elevated risk of arrest and recidivism (Hoggatt et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMost existing research on JIVs relies on treatment-seeking or incarcerated samples, limiting the generalizability to the broader veteran population (Tsai et al., 2016; Blonigen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Importantly, justice involvement is often examined as a binary outcome, failing to account for arrest frequency as a potential marker of more severe psychopathological risk. Given these current gaps in literature, the present study utilizes data from the National Health and Resilience in Veterans Study (NHRVS), a nationally representative survey of U.S. veterans. By categorizing arrest history into three groups (no arrests, one arrest, and two or more arrests) this study aims to shine a light on the impact of recidivism. Specifically, we aimed to (1) identify sociodemographic, military, and trauma-related characteristics associated with arrest frequency; and (2) examine whether arrest frequency is independently associated with current mental health problems, including substance use and suicidality. Findings from this research can inform early identification and tailored interventions for at-risk veterans across both VA and non-VA healthcare settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eData were analyzed from the National Health and Resilience in Veterans Study (Fogle et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wisco et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which surveyed a nationally representative sample of 4,069 US veterans. The NHRVS sample was ascertained from KnowledgePanel\u0026reg;, a research panel maintained by the survey research firm Ipsos, which consists of more than 50,000 households. KnowledgePanel\u0026reg; is an online, probability-based, non-volunteer access survey panel of U.S. adults that covers approximately 98% of U.S. households. Participants completed a 50-minute online survey between 11/18/19 and 3/8/20 (median completion date: 11/21/19). Participants were recruited through national random sampling via telephone and postal mail. Internet and computer access were provided if needed. Post-stratification weights based on demographic distributions of US veterans from concurrent US Census data were applied in inferential analyses. This raking adjustment of weights, which was conducted by the Ipsos statistical team, used benchmark distributions of U.S. military veterans from the most contemporaneous August 2019 Current Veteran Population Supplemental Survey and included gender, age, race/ethnicity, Census region, metropolitan status, education, household income, branch of military service, and years of military service.\u003c/p\u003e\u003cp\u003e All participants provided electronic informed consent prior to participation in the study. The study protocol was reviewed and approved by the Institutional Review Board of the VA Connecticut Healthcare System. All procedures were conducted in accordance with the Declaration of Helsinki and relevant regulatory standards.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSample\u003c/h2\u003e\u003cp\u003eParticipants were recruited through national random sampling using telephone and postal mail outreach. Participants were eligible if they were U.S. military veterans aged 18 or older. Internet and computer access were provided to participants if requested. Post-stratification weights based on demographic distributions of participants from U.S. Census data were applied to enhance generalizability to broader veteran populations. The current sample was drawn from KnowledgePanel\u0026reg;, a probability-based, non-volunteer access survey panel maintained by Ipsos. KnowledgePanel\u0026reg; includes over 50,000 households and covers approximately 98% of U.S. households.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eThe NHRVS survey assessed a wide range of demographics, including sociodemographic characteristics, military history, age, gender, race/ethnicity, education, marital status, employment status, and household income. A detailed list of all measures used is presented in Supplemental Table\u0026nbsp;1.\u003c/p\u003e\n\u003ch3\u003eArrest History\u003c/h3\u003e\n\u003cp\u003eArrest history was assessed with the question: \u003cem\u003e\u0026ldquo;In your lifetime, were you ever arrested?\u0026rdquo;\u003c/em\u003e Veterans who responded \u0026ldquo;yes\u0026rdquo; were asked a follow-up question: \u003cem\u003e\u0026ldquo;How many times?\u0026rdquo;\u003c/em\u003e Responses were re-coded into a three-level variable representing 0, 1, or 2\u0026thinsp;+\u0026thinsp;lifetime arrests. Participants who endorsed any history of arrest were also asked to identify the nature of the offense(s) they were arrested for.\u003c/p\u003e\n\u003ch3\u003eSociodemographic and Military Variables\u003c/h3\u003e\n\u003cp\u003eThe following sociodemographic variables were assessed: age, gender, race/ethnicity, marital status, education, employment/retirement status, household income, and whether the veteran used the VA as their primary source of healthcare. Military service characteristics included era of service and exposure to combat.\u003c/p\u003e\n\u003ch3\u003eTrauma and Psychopathology\u003c/h3\u003e\n\u003cp\u003eTrauma exposure was assessed via cumulative count of Adverse Childhood Experiences (ACEs) and lifetime trauma events. Specific trauma and ACES were used, as seen in Supplemental Table\u0026nbsp;1. Psychiatric variables included lifetime and current diagnoses of major depressive disorder, direct and indirect traumas, military sexual traumas, posttraumatic stress disorder, alcohol and substance use disorders, suicide attempts and ideation, anxiety disorders, and gambling disorder.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData Analyses\u003c/h2\u003e\u003cp\u003eItem-level missing data (\u0026lt;\u0026thinsp;5%) were imputed using chained equations. First, analyses of variance and chi-square tests were conducted to compare characteristics between veterans with no, 1, and 2\u0026thinsp;+\u0026thinsp;arrests. Second, a multinomial logistic regression analysis was conducted to identify sociodemographic, military, and trauma-related variables that differentiated veterans with 1 and 2\u0026thinsp;+\u0026thinsp;arrests; two sets of analyses with different reference categories were conducted to enable comparison of all groups (i.e., 1 and 2\u0026thinsp;+\u0026thinsp;vs. 0 arrests; 2\u0026thinsp;+\u0026thinsp;arrests vs. 1 arrest) Third, a series of binary multivariable logistic regression analyses were conducted to examine association between arrest history and psychiatric variables. Sociodemographic, military, and trauma-related variables were adjusted in these analyses; analyses of suicidality variables were additionally adjusted for lifetime psychiatric and substance use disorders.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe overall sample was 90.2% male, with 78.1% identifying as White, 11.2% as Black, 6.6% as Hispanic, and 4.2% as another race or multiracial, and had a mean age of 62.2 years (SD\u0026thinsp;=\u0026thinsp;15.7). Nearly one-third (32.7%) had obtained a college degree or higher education, 72.4% were married or living with a partner, and 58.5% reported a household income above \u003cspan\u003e$\u003c/span\u003e60,000. Nearly half of the sample (44.3%) was retired. Of all respondents, 49.7% served in the Army, 21.2% in the Navy, 19.6% in the Air Force, and 6.5% in the Marine Corps. Slightly more than one-third of veterans reported having been exposed to combat (35.0%).\u003c/p\u003e\u003cp\u003eA total of 2,939 (69.9%) of veterans denied ever having been arrested, and 642 (16.7%) and 468 (13.4%) reported 1 or 2+ (mean\u0026thinsp;=\u0026thinsp;3.5, SD\u0026thinsp;=\u0026thinsp;3.1, range\u0026thinsp;=\u0026thinsp;2\u0026ndash;32) arrests, respectively. Veterans with 2\u0026thinsp;+\u0026thinsp;arrests were more likely than those with 1 arrest to have been incarcerated (14.3% vs. 8.2%, χ2(1)\u0026thinsp;=\u0026thinsp;11.59, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and to report substance-related offenses (43.5% vs. 33.5%) and less likely to report criminal-mischief-related offenses (2.2% vs. 5.7%), χ2(8)\u0026thinsp;=\u0026thinsp;20.85, p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (top) shows results of analyses examining sociodemographic, military, and trauma-related correlates of arrest history. A multivariable analysis revealed that, relative to veterans with no arrest history, those with 1 or 2\u0026thinsp;+\u0026thinsp;arrests were younger and more likely to have lower education and not be retired; they also reported more adverse childhood experiences (ACEs) and traumas. Veterans with 2\u0026thinsp;+\u0026thinsp;arrests were additionally more likely to be male, Black, and unmarried/partnered, to have lower household incomes, and to use the VA as their primary source of healthcare. Relative to veterans with 1 arrest, those with 2\u0026thinsp;+\u0026thinsp;arrests were more likely to be male, non-White, and unmarried/partnered, to have lower education and household incomes, and to use the VA as their primary source of healthcare.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelates of arrest history among U.S. military veterans\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eSociodemographic, Military, and Trauma-Related Correlates\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Arrests\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,939\u003c/p\u003e\u003cp\u003e(weighted 69.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003eArrest\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;642\u003c/p\u003e\u003cp\u003e(weighted 16.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2+\u003c/p\u003e\u003cp\u003eArrests\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;468\u003c/p\u003e\u003cp\u003e(weighted 13.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 Arrest\u003c/p\u003e\u003cp\u003evs.\u003c/p\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u0026thinsp;+\u0026thinsp;Arrests\u003c/p\u003e\u003cp\u003evs.\u003c/p\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u0026thinsp;+\u0026thinsp;Arrests\u003c/p\u003e\u003cp\u003evs.\u003c/p\u003e\u003cp\u003e1 Arrest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e(weighted %)\u003c/p\u003e\u003cp\u003eor\u003c/p\u003e\u003cp\u003eWeighted mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e(weighted %) or\u003c/p\u003e\u003cp\u003eWeighted mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e(weighted %) or\u003c/p\u003e\u003cp\u003eWeighted mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e or F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAdjusted OR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAdjusted OR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAdjusted OR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (in years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.0 (15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.1 (16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.3 (14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.98 (0.98\u0026ndash;0.99)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.98 (0.98\u0026ndash;0.99)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (0.98\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale sex (vs female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2526 (89.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e577 (90.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e443 (95.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.19 (0.80\u0026ndash;1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.38 (1.34\u0026ndash;4.21)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.96 (1.03\u0026ndash;3.87)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite (vs non-White)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2459 (80.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e512 (78.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e333 (67.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.89 (0.66\u0026ndash;1.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.51 (0.38\u0026ndash;0.69)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.58 (0.40\u0026ndash;0.84)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack (vs non-Black)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e188 (10.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (10.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53 (16.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.07 (0.71\u0026ndash;1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.69 (1.13\u0026ndash;2.51)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.57 (0.94\u0026ndash;2.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/partnered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2141 (75.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e451 (74.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e281 (56.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.97 (0.76\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.42(0.33\u0026ndash;0.55)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.43 (0.31\u0026ndash;0.60)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege degree or higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1429 (36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e266 (30.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e122 (16.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e85.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.76 (0.60\u0026ndash;0.96)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.34 (0.25\u0026ndash;0.45)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.45 (0.32\u0026ndash;0.62) ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1656 (46.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e343 (39.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e217 (35.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.75 (0.60\u0026ndash;0.94)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.63 (0.49\u0026ndash;0.81)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.84 (0.62\u0026ndash;1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual household income \u0026gt;\u003cspan\u003e$\u003c/span\u003e60,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1783 (61.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e372 (61.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e194 (37.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e113.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.99 (0.79\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.37 (0.29\u0026ndash;0.48)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.37 (0.27\u0026ndash;0.51)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombat veteran\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e946 (34.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e241 (39.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e160 (34.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.26 (0.99\u0026ndash;1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.04 (0.79\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.82 (0.59\u0026ndash;1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVA primary healthcare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e496 (18.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e141 (21.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e148 (29.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.19 (0.90\u0026ndash;1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.87 (1.41\u0026ndash;2.48)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.57 (1.10\u0026ndash;2.24)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdverse childhood experiences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.2 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.26 (1.19\u0026ndash;1.34)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.21 (1.14\u0026ndash;1.28)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.04 (0.97\u0026ndash;1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal traumas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.3 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.7 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.0 (10.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.02 (1.01\u0026ndash;1.04)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.03 (1.02\u0026ndash;1.04)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.99 (0.97\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eNote\u003c/em\u003e. OR\u0026thinsp;=\u0026thinsp;odds ratio; 95%CI\u0026thinsp;=\u0026thinsp;95% confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between arrest frequency and psychiatric variables among U.S. military veterans\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eArrest\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,939\u003c/p\u003e\u003cp\u003e(weighted 69.9%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003eArrest\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;642\u003c/p\u003e\u003cp\u003e(weighted 16.7%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2+\u003c/p\u003e\u003cp\u003eArrests\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;468\u003c/p\u003e\u003cp\u003e(weighted 13.4%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 Arrest\u003c/p\u003e\u003cp\u003evs.\u003c/p\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u0026thinsp;+\u0026thinsp;Arrests\u003c/p\u003e\u003cp\u003evs.\u003c/p\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u0026thinsp;+\u0026thinsp;Arrests\u003c/p\u003e\u003cp\u003evs.\u003c/p\u003e\u003cp\u003e1 Arrest\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e(weighted %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e(weighted %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e(weighted %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAdjusted OR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAdjusted OR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAdjusted OR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMajor depressive disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174 (7.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55 (12.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.89 (0.65\u0026ndash;1.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.02 (0.74\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.20 (0.81\u0026ndash;1.77)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePosttraumatic stress disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.72 (0.49\u0026ndash;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.20 (0.83\u0026ndash;1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.47 (0.92\u0026ndash;2.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneralized anxiety disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e147 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (12.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.01 (0.73\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.23 (0.88\u0026ndash;1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.18 (0.78\u0026ndash;1.76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol use disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e191 (7.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86 (15.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83 (18.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.92 (1.48\u0026ndash;2.48)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.36 (1.80\u0026ndash;3.10)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.25 (0.92\u0026ndash;1.71)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug use disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e158 (6.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (10.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89 (24.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e160.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.42 (1.05\u0026ndash;1.93)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.10 (2.35\u0026ndash;4.09)***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.34 (1.66\u0026ndash;3.29)***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProblem gambling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111 (3.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (7.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (7.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.59 (1.11\u0026ndash;2.27)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.51 (1.02\u0026ndash;2.23)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.90 (0.56\u0026ndash;1.44)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuicide attempt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (6.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.74 (0.46\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.14 (0.74\u0026ndash;1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.93 (1.10\u0026ndash;3.39)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuicidal ideation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200 (7.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (9.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 (14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.88 (0.65\u0026ndash;1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.08 (0.80\u0026ndash;1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.54 (1.06\u0026ndash;2.24)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuicidal intent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.02 (0.51\u0026ndash;2.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.49 (0.79\u0026ndash;2.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.52 (0.70\u0026ndash;3.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote\u003c/em\u003e. OR\u0026thinsp;=\u0026thinsp;odds ratio; 95%CI\u0026thinsp;=\u0026thinsp;95% confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eOdds ratios for analyses of psychiatric correlates are adjusted for age, sex, race/ethnicity, marital status, education, retirement status, household income, combat veteran status, primary source of healthcare, adverse childhood experiences, and cumulative trauma histories. Analyses of suicide-related variables are additionally adjusted for lifetime major depressive, posttraumatic stress, and alcohol, drug, and nicotine use disorders.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (bottom) shows results of analyses examining psychiatric variables by arrest history. Multivariable analyses revealed that, relative to veterans with no arrest history, those with 1 or 2\u0026thinsp;+\u0026thinsp;arrests were more likely to screen positive for alcohol and drug use disorders and problem gambling. Further, relative to veterans with 1 arrest, those with 2\u0026thinsp;+\u0026thinsp;arrests were more likely to screen positive for drug use disorders and to report a suicide attempt and current suicidal ideation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to examine the prevalence and correlates of arrest frequency in a contemporary, nationally representative sample of veterans. Results extend to the U.S. veteran population prior work documenting an association between frequency of recidivism and mental illness (Dalbir et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The current study further extends this work to identify sociodemographic factors linked to arrest frequency in this population. While past research (Holloway et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) has suggested that Black, indigenous, and other people of color (BIPOC) males are more likely to be repeatedly arrested than their White counterparts, this is the first study to observe these findings in a veteran sample, as well as compare single and multiple arrests as separate variables. To our knowledge, extant studies that assess the relationship between education and recidivism rates have been conducted exclusively within juvenile populations (Fox et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Azad \u0026amp; Ginner Hau, 2020; Robertson et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Results of the present study extend this work to suggest that lower levels of education are associated with repeated arrests among veterans and could be a target of intervention for this population.\u003c/p\u003e\u003cp\u003ePrior research suggests strong connections between history of arrests and mental and substance use disorders in JIVs (Blodgett et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Hoggatt et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Inoue et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Further, Edwards and colleagues (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that 47% of veterans at risk for suicide had a history of criminal arrests and co-occurring substance use disorders. After adjusting for sociodemographic, military, trauma, and lifetime psychiatric variables, veterans with 2\u0026thinsp;+\u0026thinsp;arrests had 2-fold greater odds of screening positive for current drug use disorders and having attempted suicide, and approximately 1-in-7 had recently contemplated suicide. While prior work has linked recidivism to suicide in adolescents (Mallet et al., 2013), the current study extends this finding to repeated arrests in military veterans.\u003c/p\u003e\u003cp\u003eCollectively, these results underscore the importance of history of repeated arrests as a potential risk factor for adverse mental health outcomes in veterans and suggest considering legal concerns as part of suicide risk assessments in this population. Links with problem gambling may be particularly relevant given changes in sports and internet gambling (Grubbs \u0026amp; Kraus, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Regarding treatment, the Collaborative Chronic Care Model (CCM; Kim et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) may help minimize the adverse effects of substance use disorders post-incarceration in veterans, and mental health court programs may help mitigate risk of repeated arrests (Fox et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Various local criminal justice partners and stakeholders working with the VA\u0026rsquo;s Veterans Justice Programs to connect eligible justice-involved veterans with VA healthcare and social services may also be beneficial (Tsai et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These findings also suggest that targeted interventions may be most impactful if implemented prior to or immediately following a first arrest, ideally within VA or community mental health systems. Primary care providers, VA case managers, and community outreach workers are well-positioned to identify at-risk veterans and initiate early intervention. Given that most veterans are not engaged in VA health care services (Kline et al., 2022), further work is needed to implement and evaluate the effectiveness of training programs, resource sharing, collaborative care models, referral pathways, and veteran-centered care initiatives for veterans outside the VA system.\u003c/p\u003e\u003cp\u003eWhile both veterans and general civilians share similar rates of incarceration (~\u0026thinsp;33% of American civilians and ~\u0026thinsp;30% of veterans endorsed CJI), mental disorders are more frequent in veterans compared to their justice-involved adult counterparts (Blodgett et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Among treatment-seeking veterans, previous criminal history is relatively common, with Blonigen and colleagues (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reporting that 94% of veterans in their treatment program had past CJI, but no known studies have investigated the association between repeated arrests and mental health in community-dwelling veterans. Of note, our data suggest that veterans with a history of repeated arrests are more likely to seek help through the VA. The VA offers short-term outpatient counseling, intensive outpatient treatment, marital and family counseling, self-help (e.g., 12-step) groups, residential care, and relapse prevention (U.S. Department of Veterans Affairs, 2023). Community, peer, and family support often contribute importantly to reintegration for previously incarcerated veterans suffering from substance use disorders, and if implemented efficiently, may prevent future incarcerations and promote long-lasting recovery (Blonigen, 2020).\u003c/p\u003e\u003cp\u003eRegarding other intervention strategies, studies have shown the importance of social support and connectedness when transitioning back into civilian society to decrease likelihood of repeat arrests and enhance recovery from substance use disorders among veterans (Blonigen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e \u0026amp; Hoopsick et al., 2019). While models such as the Collaborative Chronic Care Model (CCM; Kim et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) have been implemented to minimize substance use disorders, research also suggests that mental health court programs correspond to a moderate reduction in for adults (Fox et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and could be impactful for veteran populations. As emphasized by Tsai and colleagues (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), CJI among veterans is of national concern, and continued research is needed to understand the epidemiology, treatment and services, role of justice, community, and healthcare systems and their interface, methodology and research resources, and policies related to CJI in this population. Additionally, veterans treatment courts (i.e., specialized court diversion programs tailored to justice-involved veterans) may be uniquely suited to address the co-occurring legal and behavioral health needs of this population and warrant further evaluation (Fox et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eWhile the present study is limited by the cross-sectional design, predominantly older, male, and White, non-Hispanic sample, and use of self-report measures, these limitations are counterbalanced by the large, nationally representative dataset, which yielded results that generalize to the U.S. veteran population. However, it is worth noting that the average age of participants in the sample was relatively high, which may limit generalizability to younger veterans or those who have more recently separated from service. Additionally, given the cross-sectional design of this study, we cannot determine the directionality or causality of associations between arrest frequency and mental health outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eFuture Directions\u003c/h2\u003e\u003cp\u003eWe recommend future research to collect more detailed data on veterans\u0026rsquo; criminal justice involvement, including the specific nature, timing, and context of arrests. Grouping veterans with two or more arrests together may obscure important differences in risk factors and mental health outcomes between those with relatively few versus frequent arrests. A more nuanced understanding of criminal history patterns could inform tailored interventions targeted to distinct subgroups of justice-involved veterans. Additionally, longitudinal studies tracking changes over time could help clarify causal pathways between repeated arrests and mental health trajectories.\u003c/p\u003e\u003cp\u003eResults highlight the importance of repeated arrests in relation to behavioral health concerns in veterans. However, further research is needed to examine the generalizability of results to higher-risk veterans; identify mechanisms underlying links between CJI and adverse mental health outcomes; and evaluate effectiveness of intervention strategies to help mitigate CJI and repeated arrests in this and other at-risk populations.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.J. and A.M. wrote main manuscript. A.J. conceptualized manuscript topic and organized meetings, writing, and coordination between authorsR.B. and M.P. supervised all work and provided data set, as well as mentorship to A.J. and A.M. R.B. and M.P. revised and supervised data analyses and methods sections . J.S. provided information and education on this specific population, as well as aiding in editing paper.E.S. contributed to data analyses. A.J. and E.S. prepared all tables.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAzad, A., \u0026amp; Ginner Hau, H. (2020, April). Adolescent females with limited delinquency: A follow-up on educational attainment and recidivism. In Child \u0026amp; Youth Care Forum (Vol. 49, pp. 325-342). Springer US.\u003c/li\u003e\n\u003cli\u003eBlodgett, J. C., Avoundjian, T., Finlay, et al. (2015). Prevalence of Mental Health Disorders Among Justice-Involved Veterans. Epidemiologic Reviews, 37(1), 163\u0026ndash;176. https://doi.org/10.1093/epirev/mxu003\u003c/li\u003e\n\u003cli\u003eBlonigen, D. M., Macia, K. S., Smelson, D., et al. (2020). Criminal recidivism among justice-involved veterans following substance use disorder residential treatment. Addictive Behaviors, 106.\u003c/li\u003e\n\u003cli\u003eDalbir, N., Wright, E. M., \u0026amp; Steiner, B. (2022). Mental Illness, Substance Use, and Co-Occurring Disorders among Jail Inmates: Prevalence, Recidivism, and Gender Differences. Corrections, 1\u0026ndash;23. https://doi.org/10.1080/23774657.2022.2090028\u003c/li\u003e\n\u003cli\u003eDeahl, M. P., Klein, S., \u0026amp; Alexander, D. A. (2011). The costs of conflict: Meeting the mental health needs of serving personnel and service veterans. International Review of Psychiatry, 23(2), 201\u0026ndash;209. https://doi.org/10.3109/09540261.2011.557059\u003c/li\u003e\n\u003cli\u003eEdwards, E. R., Epshteyn, G., Connelly, B., Redden, C., Moussa, C. E. H., Blonigen, D. M., ... \u0026amp; Osterberg, T. (2024). Understanding criminogenic risk factors among United States military veterans: An updated literature review. Criminal Justice Review, 49(4), 495-518. https://doi.org/10.1177/07340168231160862 \u003c/li\u003e\n\u003cli\u003eEdwards, E. R., Gromatsky, M., Sissoko, D. R. G., et al. (2022). Arrest history and psychopathology among veterans at risk for suicide. Psychological Services, 19(1), 146\u0026ndash;156. https://doi.org/10.1037/ser0000454 \u003c/li\u003e\n\u003cli\u003eFogle, B. M., Tsai, J., Mota, N., et al. (2020). The National Health and Resilience in Veterans Study: A Narrative Review and Future Directions. Frontiers in Psychiatry, 11. https://doi.org/10.3389/fpsyt.2020.538218\u003c/li\u003e\n\u003cli\u003eFox, B., Miley, L. N., Kortright, K. E., et al. (2021). Assessing the Effect of Mental Health Courts on Adult and Juvenile Recidivism: A Meta-Analysis. American Journal of Criminal Justice, 46(4), 644\u0026ndash;664. https://doi.org/10.1007/s12103-021-09629-6\u003c/li\u003e\n\u003cli\u003eGrubbs, J. B., \u0026amp; Kraus, S. W. (2023). The relative risks of different forms of sports betting in a U.S. sample: A brief report. Comprehensive Psychiatry, 127. https://doi.org/10.1016/j.comppsych.2023.152420\u003c/li\u003e\n\u003cli\u003eHoggatt, K. J., Harris, A. H., Washington, D. L., et al. (2021). Prevalence of substance use and substance-related disorders among US Veterans Health Administration patients. Drug and Alcohol Dependence, 225.\u003c/li\u003e\n\u003cli\u003eHolloway, E. D., Folk, J. B., Ordorica, C., et al. (2022). Peer, substance use, and race-related factors associated with recidivism among first-time justice-involved youth. Law and Human Behavior, 46(2), 140\u0026ndash;153. https://doi.org/10.1037/lhb0000471\u003c/li\u003e\n\u003cli\u003eHoopsick, R. A., Homish, D. L., Lawson, S. C., et al. (2022). Drug use over time among never‐deployed US Army Reserve and National Guard soldiers: The longitudinal effects of non‐deployment emotions and sex. Stress and Health, 38(5), 1045\u0026ndash;1057. https://doi.org/10.1002/smi.3156\u003c/li\u003e\n\u003cli\u003eInoue, C., Shawler, E., Jordan, C. H., et al. (2023). Veteran and Military Mental Health Issues. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK572092/\u003c/li\u003e\n\u003cli\u003eKline, A. C., Panza, K. E., Nichter, B., Tsai, J., Harpaz-Rotem, I., Norman, S. B., \u0026amp; Pietrzak, R. H. (2022). Mental Health Care Use Among U.S. Military Veterans: Results From the 2019-2020 National Health and Resilience in Veterans Study. Psychiatric Services (Washington, D.C.), 73(6), 628\u0026ndash;635. https://doi.org/10.1176/appi.ps.202100112 \u003c/li\u003e\n\u003cli\u003eKim, B., Bolton, R. E., Hyde, J., et al. (2019). Coordinating across correctional, community, and VA systems: applying the Collaborative Chronic Care Model to post-incarceration healthcare and reentry support for veterans with mental health and substance use disorders. Health \u0026amp; Justice, 7, 1-12.\u003c/li\u003e\n\u003cli\u003eMallett, C. A., Fukushima, M., Stoddard-Dare, P., et al. (2013). Factors related to recidivism for youthful offenders. Criminal Justice Studies, 26(1), 84\u0026ndash;98. https://doi.org/10.1080/1478601X.2012.705539 \u003c/li\u003e\n\u003cli\u003eRobertson, A.A., Fang, Z., Weiland, D., et al. (2020). Recidivism among justice-involved youth: Findings from JJ-TRIALS. Criminal justice and behavior, 47(9), pp.1059-1078.\u003c/li\u003e\n\u003cli\u003eSiennick, S.E., Widdowson, A.O. Juvenile Arrest and Later Economic Attainment: Strength and Mechanisms of the Relationship. J Quant Criminol 38, 23\u0026ndash;50 (2022). https://doi.org/10.1007/s10940-020-09482-6 \u003c/li\u003e\n\u003cli\u003eTimko, C., Nash, A., Owens, M. D., Taylor, E., \u0026amp; Finlay, A. K. (2020). Systematic review of criminal and legal involvement after substance use and mental health treatment among veterans: Building toward needed research. Substance Abuse: Research and Treatment, 14. https://doi.org/10.1177/117822181990128\u003c/li\u003e\n\u003cli\u003eTsai, J., Flatley, B., Kasprow, W. J., et al. (2017). Diversion of Veterans With Criminal Justice Involvement to Treatment Courts: Participant Characteristics and Outcomes. Psychiatric Services, 68(4), 375\u0026ndash;383. https://doi.org/10.1176/appi.ps.201600233\u003c/li\u003e\n\u003cli\u003eTsai, J., Kelton, K., Blonigen, D. M., et al. (2023). A Research Agenda for Criminal Justice Involvement Among U.S. Veterans. Military Medicine, usad201. https://doi.org/10.1093/milmed/usad201\u003c/li\u003e\n\u003cli\u003eU.S. Department of Veterans Affair. (2023). VA.gov | Veterans Affairs [Program Homepage]. https://www.ptsd.va.gov/\u003c/li\u003e\n\u003cli\u003eWisco, B. E., Nomamiukor, F. O., Marx, B. P., Et al. (2022). H. Posttraumatic Stress Disorder in US Military Veterans: Results From the 2019-2020 National Health and Resilience in Veterans Study. Journal of Clinical Psychiatry, 83(2), 20m14029. https://doi.org/ 10.4088/JCP.20m14029 \u003c/li\u003e\n\u003cli\u003eYen, R. (2023). From Service to Sentencing: Unraveling Risk Factors for Criminal Justice Involvement Among U.S. Veterans. Council on Criminal Justice. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"veterans, repeated arrests, substance use, suicide, incarceration, mental health","lastPublishedDoi":"10.21203/rs.3.rs-7861353/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7861353/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eINTRODUCTION: Military veterans experience higher rates of mental disorders compared to non-veterans, and criminal justice system involvement (CJI) may exacerbate these conditions. Despite this, few population-based studies have examined the prevalence, correlates, and mental health burden associated with CJI among veterans.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMETHODS: Data were analyzed from the National Health and Resilience in Veterans Study, a nationally representative survey of 4,069 U.S. veterans. Prevalence of CJI and its sociodemographic and clinical correlates were assessed, comparing veterans with no arrests, one arrest, and two or more arrests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRESULTS: Overall, 30.1% of veterans reported a history of CJI, including 16.7% with one arrest and 13.4% with two or more arrests. Veterans with multiple arrests were more likely to be male, from racial or ethnic minority groups, unmarried or unpartnered, of lower educational attainment and income, and to utilize VA healthcare. They also exhibited significantly higher rates of current drug use disorders, suicidal ideation, and suicide attempts compared with veterans with one or no arrests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCONCLUSION: CJI is common among U.S. veterans and is associated with substantial sociodemographic disadvantage and mental health burden. These findings underscore the need for targeted prevention and intervention efforts—particularly for veterans with repeated arrest histories—to address substance use, suicidality, and related psychosocial risk factors.\u003c/p\u003e","manuscriptTitle":"Repeated arrests and associated mental health characteristics in U.S. veterans: Results from a nationally representative study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-01 07:45:59","doi":"10.21203/rs.3.rs-7861353/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6dca6520-bc44-4ada-9074-e6e2894ac83d","owner":[],"postedDate":"November 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-01T07:45:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-01 07:45:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7861353","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7861353","identity":"rs-7861353","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}