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Offending behaviour, health and wellbeing of military veterans in the criminal justice system

  • Roxanna Short ,

    Roles Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

    roxanna.short@kcl.ac.uk

    Affiliation Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom

  • Hannah Dickson,

    Roles Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing – review & editing

    Affiliation Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom

  • Neil Greenberg,

    Roles Conceptualization, Funding acquisition, Supervision, Writing – review & editing

    Affiliation King’s Centre for Military Health Research, King’s College London, London, United Kingdom

  • Deirdre MacManus

    Roles Conceptualization, Funding acquisition, Methodology, Supervision, Writing – review & editing

    Affiliation Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom

Offending behaviour, health and wellbeing of military veterans in the criminal justice system

  • Roxanna Short, 
  • Hannah Dickson, 
  • Neil Greenberg, 
  • Deirdre MacManus
PLOS
x

Abstract

Background

A small but significant proportion of military veterans become involved in the criminal justice system (CJS) after leaving service. Liaison and Diversion (L&D) services aim to identify vulnerable offenders in order to provide them with the health/welfare support they need, and (where possible) divert them away from custody. An administrative database of L&D service-users was utilised to compare the needs of veterans with those of non-veteran L&D service-users.

Method

National data collected from 29 L&D services in 2015–2016 was utilised. Of the 62,397 cases, 1,067 (2%) reported previous service in the Armed Forces, and 48,578 had no previous service history. The associations between veteran status and socio-demographic characteristics, offending behaviour, health- and mental health-problems were explored. The associations between specific types of offending and mental health problems within the veterans in the sample were also investigated.

Results

Veterans tended to be older, and less likely to be unemployed than non-veterans, but just as likely to have unstable living arrangements (including homelessness). Veteran status was associated with increased levels of interpersonal violence, motoring offences, anxiety disorders and hazardous drinking patterns. Veteran status was associated with decreased levels of acquisitive offending, schizophrenia, ADHD, and substance misuse. Among veterans, the presence of an anxiety disorder (umbrella term which included GAD, Phobias, PTSD etc.) was associated with increased interpersonal violence, alcohol misuse was associated with increased motoring offences, and substance use was associated with increased acquisitive offending.

Conclusions

Our study indicates that among offenders in the CJS who have been identified as needing health or welfare support, veterans differ from non-veterans in terms of their health and welfare needs and offending behaviour. These differences may be influenced by the impact of military service and the transition into civilian life. Our findings support the identification of military personnel within the CJS to provide appropriate interventions and support to improve outcomes and reduce offending.

Introduction

The majority of service leavers make successful transitions back into civilian life [1]. However, recent research has shown that a minority, who become involved in the Criminal Justice System (CJS), often experience health, behavioural and social problems [2]. Estimates of the proportion of the prison population who have previously served in the UK Armed Forces have ranged from 3.5% to 17% depending on the methodology and sample used [35]. Furthermore, it was estimated that 3.4% of adults subject to probation supervision in England and Wales in 2009 were veterans [6]. A government review of ex-Armed Forces Personnel in the CJS [7] highlighted the need for better identification of the needs of veterans in the CJS to inform the development of services to help reduce re-offending. Previous research into the needs of veterans in the CJS has been limited by the use of selected samples from specific areas of the CJS, such as prison or probation, with limited data on health and welfare needs [8].

Government statistics show that veterans form the largest single occupational group within the prison and probation services, and that they are more likely to have committed a violent or sexual offence than offenders who have not served in the military [3,6]. However, these statistics relate to those who are given a custodial sentence or probation supervision order. Many offenders will not receive either category of sentence. Risk factors for offending in veterans are largely similar to those for civilians (i.e. younger age, male gender, lower socioeconomic class, history of offending) [9,10], but with some notable exceptions. Veterans who offend tend to be older, on average, than general population offenders [3]. This may be because military service acts to reduce the opportunity for offending (or the risk of conviction) at a time when young men in the general population are most at risk of offending, and thus the individual’s time in service acts as a “hiatus” from offending that may have occurred anyway [8]. Prior research also indicates that experiences resulting from military service can increase the risk of offending in some individuals [11], suggesting a distinct pathway to offending in this particular group. For example, certain aspects of deployment, such as deploying in a combat role and exposure to trauma during deployment, have been shown to increase the risk of violent offending by military personnel on return [1113]. Finally, some but not all mental health problems, such as Posttraumatic Stress Disorder (PTSD) and alcohol misuse, have been shown to increase the risk of offending behaviour among military personnel [11,14].

Existing research also suggests that socioeconomic needs, such as relationship problems, financial instability, unemployment and lack of stable accommodation, are strong risk factors for offending among veterans and that stability in these areas can be protective against the risk of offending in the presence of mental health problems [13,15,16]. Therefore, by the identification of socioeconomic and mental health needs among veterans on entry into the CJS we have the potential to inform the development of early intervention services for veterans.

The purpose of Liaison and Diversion (L&D) services is to provide an assessment of individuals within the CJS who have been identified as having a psychosocial need (for example, mental health problems, learning difficulties, physical health problems, alcohol- and substance-use problems, welfare needs), to ensure that they receive the appropriate support, and to (where possible/appropriate) divert them out of the CJS and into health, social care or other services. Referral to L&D services usually occurs at the earliest opportunity, but may take place at any stage of the CJS. Furthermore, referrals can be made by a wide range of agencies, including: police, Crown Prosecution Service, youth offending teams, social workers, drugs/alcohol services, defence lawyers, and parents/guardians/family members. This broad referral process maximises the reach of the L&D service to the most vulnerable of individuals at the earliest opportunity. Individuals referred to L&D services are offered a screening appointment with a mental health practitioner, during which they are asked whether they have ever served in the UK Armed Forces. To date, there has been no formal comparison of L&D service offenders with offenders who have not been referred to these services. However, it is likely that those offenders referred to L&D services represent a particularly vulnerable group of offenders with a range of psychosocial vulnerabilities.

The research to date has focused either on aspects of military service that are risk factors for offending, or comparisons of offending between veterans and non-veterans within the CJS without considering other factors such as health, mental health and welfare needs. Our study aims to address this gap in the literature. We utilised the national L&D database of all offenders, both Armed Forces veterans and non-veterans, who were assessed by the 29 services across England during 2015–2016. Our primary aim was to identify differences between veterans and non-veterans within L&D services, in terms of: socioeconomic and welfare needs; offending behaviour; and health and mental health problems. In addition, we examined factors associated with specific types of offending (interpersonal violence, acquisitive and motoring offences) among veterans referred to L&D services.

Methods

Sample and procedure

This study employed routinely-collected data from 29 separate L&D services from April 2015 until April 2016. Data were gathered by L&D service practitioners, and included information pertaining to the individual’s: military status; socio-demographics; current offence; mental health needs; alcohol/substance use; and other vulnerabilities (learning, physical, or social and communication difficulties). Data were entered onto the database on a case-by-case basis: each referral was treated as a separate case. Thus, in the absence of an individual identifier, the same individual may have multiple entries relating to different offences. As shown in Fig 1, a total of 62,397 referrals were made to the 29 L&D sites between April 2015 and April 2016. Of these, 49,793 (80%) cases included information regarding the individual’s military status, 1,215 (2.4%) of which pertained to individuals who reported that they had served, or were currently serving, in the UK Armed Forces. Given that the majority of the military personnel in the database were veterans, we categorised the cases with a recorded military status as veterans (N = 1,067; includes individuals who left military service within the last 12 months, 1–5 years ago, or more than 5 years ago), or non-veterans (N = 48,578), and excluded those who reported that they were currently serving (N = 148; see Fig 1).

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Fig 1. Flow chart illustrating veteran status case selection.

https://doi.org/10.1371/journal.pone.0207282.g001

Permission to access the L&D database was sought from and approved by NHS Digital following ethical approval (reference: LRS15/162992) to ensure all information governance policies and procedures were met. The dataset was fully anonymised before it was accessed.

Measures

Veteran status.

In accordance with the UK Government’s criteria, a military veteran was classified by L&D services as anyone who had served for at least one day in the UK Armed Forces, and had left service at the time of contact with L&D services. All other non-military cases were classified as non-veterans. We note that our data are cross-sectional, thus our analyses reflect associations with veteran status, rather than its role as a risk factor for offending or physical/mental health problems.

Socio-demographics.

Socio-demographic factors included: age, gender, ethnicity (white vs black and minority ethnic; BME), employment status (employed; unemployed; sickness/disability; retired; other), and accommodation status (homeless; in temporary accommodation; in owned/rented accommodation; living with parents/relatives; other). Age was recorded in 5-year increments, and reflected the age-group of the individual at their first meeting with the L&D service. For the purposes of the statistical analyses, age was recoded in to six categories: 25 years and under, 26–35 years, 36 to 45 years, 46 to 55 years, 56 to 65 years, and over 65 years.

Current offence.

This was recorded as the most serious offence that the individual was charged with, or suspected of having committed, at the time of their referral to the L&D service. Offences were classified as: violence against the person (including murder, manslaughter, violence against the person, harassment, robbery); sex offence; non-interpersonal violence (including criminal damage, arson, possession of an offensive weapon, possession of a firearm); acquisitive offence (including theft, burglary, fraud/forgery); drug offence; public order offence; motoring offence; breach of court order; and other. For the purposes of statistical analyses, this was recoded into nine separate binary variables, each indicating the presence or absence of each offence type, in line with previous research [17]. However, for each of these variables, we are comparing the presence of one offence type with all other offence types (rather than with non-offenders, as we did not have a non-offender comparison group).

Alcohol and substance misuse.

Both alcohol and substance misuse were assessed using standardised instruments: the Alcohol Use Disorders Identification Test [18] for alcohol misuse; and the Drug Use Disorders Identification Test [19] for substance misuse. Alcohol use was categorised as hazardous (the consumption of over 14 units per week for women, and over 21 units per week for men), harmful (the individual experiences health problems that are directly related to alcohol consumption), or dependent (the individual feels unable to function without using alcohol) [18]. Presence of substance misuse was reflected by evidence of social, occupational, psychological, or physical problems related to use of drugs [19].

Wider health needs.

The presence of learning difficulties (where suspected) was established using standard cut-off scores on the Learning Disability Screening Questionnaire [20] or the Hayes Ability Screening Index [21]. Presence of social & communication difficulties (where suspected) was established using the standard cut-off score on the Autism Spectrum Quotient [22]. The presence of physical health problems was established via self-report.

Mental health.

Individuals were screened for, or diagnosed with, the following: schizophrenia, bipolar affective disorder, depression, anxiety disorders (including generalised anxiety, PTSD, phobias, panic disorder, and obsessive-compulsive disorder), adjustment disorder, eating disorders, dementia, attention deficit/hyperactivity disorder (ADHD), and personality disorder. Up to three mental health problems could be recorded for each case. Whilst all mental disorders were assessed using standardised screening tools and information from medical records where available, the screening and diagnostic methods varied among the different sites. As a consequence, some recorded mental disorders reflect actual diagnoses, whereas others reflect elevated scores on screening questionnaires.

Statistical analyses

We conducted the analyses using a series of univariate and multivariate logistic regression models in Stata 14 [23]. Standard errors were adjusted to account for the arbitrary correlation of errors across cases within each L&D site using Stata’s “cluster” algorithm [24,25]. First, we identified any socio-demographic variables that were independently associated with veteran status using a series of univariate and adjusted logistic regression analyses. These were used as covariates in the subsequent analyses comparing veterans with non-veterans. Second, we examined the univariate and adjusted associations between veteran status and each of the offence type, health needs and mental health variables in separate logistic regression models. Results from the univariate analyses are reported as odds ratios (OR) with 95% confidence intervals (CI), and results from the multivariate analyses are reported as adjusted odds ratios (aOR) with 95% CIs.

Factors associated with different types of offending among veterans

We then examined the factors associated with particular offence types in the veteran sample (N = 1,067). In these analyses, we only examined offences that were independently associated with veteran status in the preceding analyses: violence against the person (vs other non-violent and non-sexual offences); acquisitive offences (vs all other offence types); and motoring offences (vs all other offence types). As in the preceding analyses, we first identified any socio-demographic variables that were independently associated with each offence type using separate logistic regression analyses. We then conducted a series of logistic regressions to examine the univariate and adjusted associations between each offence type and each of the mental health variables. In these analyses we did not adjust the standard errors based on L&D site due to the small number of veterans per site [26]. First, we established which socio-demographic variables were independently associated with the offence type. These were retained as covariates in the following analyses. Second, we examined the univariate and adjusted associations between each of the mental health and alcohol/substance use variables and the offence type outcome.

As we were unable to identify which cases belonged to separate individuals in the database, and thus unable to assess the extent of clustering in data, we performed a crude sensitivity analysis. We matched the cases on a series of “static” variables (L&D site, gender, age, ethnicity, veteran status, presence of physical disabilities, presence of learning difficulties, and presence of social and communication difficulties), removed the duplicates (by selecting the first case in a cluster), and repeated our analyses. Our main results remained unchanged, therefore we report the results of the analysis of the full dataset.

Results

Socio-demographic characteristics

The veterans in the sample were predominantly male, aged 26–35, and of white ethnicity. These characteristics were significantly associated with veteran status (see Table 1). Veterans were more likely than non-veterans to be employed (OR = 2.63, 95% CI 2.25–3.07) or retired (OR = 8.77, 95% CI 6.54–11.75) than unemployed. Veterans were less likely than non-veterans to be living with relatives (vs. being in owned or rented accommodation; OR = 0.69, 95% CI 0.52–0.91). Veterans and non-veterans were equally likely to report being homeless (OR = 0.91, 95% CI 0.69–1.20). In the multivariate logistic regression model, gender, age, ethnicity, and employment status remained independently associated with veteran status (see Table 1), and were retained as covariates in the following multivariate analyses.

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Table 1. Association of socio-demographic factors with veteran status.

https://doi.org/10.1371/journal.pone.0207282.t001

Offence type

Offences classed as violence against the person were the most common type of offending among the whole sample (32%), followed by acquisitive (16%), public order (11%), and non-interpersonal violence offences (10%). The remaining offence types were relatively uncommon. As shown in Table 2, veterans were more likely to have committed violence against the person (OR = 1.28, 95% CI 1.10–1.49) or motoring offences (OR = 1.85, 95% CI 1.40–2.43), and less likely to have committed acquisitive offences (OR = 0.56, 95% CI 0.43–0.74) than non-veterans. These differences remained significant after adjusting for age, gender, ethnicity and employment status (see Table 2). Veterans were also more likely to have committed a sexual offence than non-veterans (OR = 1.54, 95% CI 1.10–2.14), but the multivariate model suggests that this association was driven by differences in socio-demographic characteristics (aOR = 0.77, 95% CI 0.54–1.10). The remaining offence types (drug, public order and breach offences) were not independently associated with veteran status.

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Table 2. Association of offence type with veteran status.

https://doi.org/10.1371/journal.pone.0207282.t002

Health needs

The health needs of veterans and non-veterans are shown in Table 3. Veterans were more likely than non-veterans to be recorded as having any mental health problem (OR = 1.47, 95% CI 1.18–1.82), harmful (OR = 1.28, 95% CI 1.08–1.51) or hazardous drinking (OR = 1.44, 95% CI 1.17–1.76), as well as being more likely to report physical health problems (OR = 2.03, 95% CI 1.52–2.72). Veterans were less likely than non-veterans to report substance misuse (OR = 0.49, 95% CI 0.40–0.60), learning difficulties (OR = 0.30, 95% CI 0.17–0.55), or social and communication difficulties (OR = 0.64, 95% CI 0.45–0.92). All of these health needs remained independently associated with veteran status in the multivariate models (see Table 3), except for alcohol misuse. Only hazardous drinking remained independently associated with veteran status (aOR = 1.37, 95% CI 1.09–1.71).

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Table 3. Association of health needs with veteran status.

https://doi.org/10.1371/journal.pone.0207282.t003

Mental health.

Veterans were more likely than non-veterans to be recorded as having an anxiety disorder (OR = 4.08, 95% CI 3.32–5.01), depression (OR = 1.22, 95% CI 1.04–1.44), and dementia (OR = 6.75, 95% CI 4.06–11.22). In addition, veterans were more likely to have multiple mental health problems (OR = 1.38, 95% CI 1.27–1.50). Veterans were less likely than non-veterans to be recorded as having personality disorder (OR = 0.59, 95% CI 0.46–0.78), schizophrenia (OR = 0.35, 95% CI 0.23–0.52), and ADHD (OR = 0.15, 95% CI 0.07–0.36). All of these mental health problems remained independently associated with veteran status, after adjusting for age, gender, ethnicity and employment status, except for personality disorder and depression (see Table 4).

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Table 4. Association of mental health needs with veteran status.

https://doi.org/10.1371/journal.pone.0207282.t004

Factors associated with different types of offending among veterans

Results of the multivariate logistic regression analyses for factors associated with each offence type among veterans are presented in the supporting information (see S1S6 Tables).

Violence against the person.

Age and employment status were both independently associated with violence against the person (compared to other non-violent and non-sexual offences; see S1 Table). Veterans aged 26–35 (vs. 25 and under: aOR = 0.61, 95% CI 0.37–1.00) and aged 56–65 (vs. 25 and under: aOR = 0.35, 95% CI 0.16–0.76) were less likely to have committed violence against the person than other non-violent and non-sexual offences. Veterans who were retired were more likely to have committed violence against the person than other non-violent and non-sexual offences (aOR = 2.30, 95% CI 1.05–5.04). With regard to mental health risk factors, veterans who were recorded as having an anxiety disorder (aOR = 1.42, 95% CI 1.05–1.92) were more likely to have committed violence against the person than other non-violent and non-sexual offences. Veterans who were recorded as having a problem with substance misuse (aOR = 0.51, 95% CI 0.35–0.75) were less likely to have committed violence against the person offences than other non-violent and non-sexual offences (see S2 Table). Bipolar disorder (aOR = 0.39, 95% CI 0.16–0.94) was also associated with reduced risk of violence against the person, however the low number of cases and wide confidence intervals suggest a less reliable finding (see S2 Table).

Acquisitive offences.

Employment status and accommodation status were independently associated with acquisitive offending (vs. all other offence types; see S3 Table). Veterans who were homeless (vs. owning/renting; aOR = 2.28, 95% CI 1.27–4.10) were more likely, and veterans who were employed (vs. unemployed; aOR = 0.50, 95% CI 0.29–0.88) were less likely to have committed acquisitive offences than any other offence type. Veterans who reported bipolar disorder (aOR = 3.99, 95% CI 1.73–9.21) or substance use (aOR = 3.49, 95% CI 2.17–5.63) were more likely to have committed acquisitive offences than any other offence type. Veterans reporting alcohol misuse were less likely to have committed acquisitive offences than other offence type (aOR = 0.59, 95% CI 0.37–0.94; see S4 Table).

Motoring offences.

Age and employment status were independently associated with motoring offences within the veteran sample (see S5 Table). Specifically, being employed (vs. unemployed; aOR = 3.70, 95% CI 2.12–6.58) was associated with motoring offences. Veterans aged 56–65 were more likely to have committed motoring offences than any other offence type (vs. 25 and under: aOR = 4.74, 95% CI 1.09–20.69), however the wide confidence intervals suggest a less reliable finding. Veterans reporting alcohol misuse were more likely to have committed motoring offences than any other offence type (aOR = 2.67, 95% CI 1.56–4.57; see S6 Table). Conversely, veterans reporting substance misuse were less likely to have committed motoring offences than veterans who had committed any other offence type (aOR = 0.32, 95% CI 0.12–0.83; see S6 Table).

Discussion

We know that internationally a small, but significant, proportion of Armed Forces veterans become involved in the CJS after leaving service [2729]. Research to date from the US and the UK has identified key welfare and mental health risk factors for offending among military personnel [11,30,31], but research into the needs of veterans in the CJS is lacking. Extant studies of veterans in the CJS have been limited by selected samples and limited data on health and welfare needs. This is the first UK study to utilise a national administrative database of offenders referred to a criminal justice liaison and diversion service to compare the socio-demographic characteristics, welfare needs, patterns of offending, and health and mental health needs of veterans with those of non-veteran service-users. In addition to differences in socio-demographics, key differences emerged in patterns of offending and in health and mental health needs among veterans compared to non-veterans.

There were differences in the socio-demographic characteristics of veterans compared to general population offenders: veterans were more likely to older, in employment or retired, but with just as unstable accommodation. The mean older age of veterans may be explained by military service, which delays the period during which an individual is at risk of offending in the community [11]. Higher rates of employment among veterans may result from trade or skills training acquired during military service, which may not be as available to those who have not served. The similar levels of homelessness among veterans and non-veterans suggests that military service does little to aid stability of living arrangements post-discharge [1].

An association was found between veteran status and interpersonal violence. This is consistent with previous UK and US research [9,3133]. There are a number of potential explanations for this association. Violence by military personnel has been shown to be associated with pre-enlistment antisocial behaviour [34] and the military recruits from areas of higher social deprivation and higher crime [35]. Thus, military service may merely act to temporarily contain the behaviour of individuals already predisposed [34]. We also know that deployment, in particular combat exposure, is associated with increased risk of future violence among veterans even after adjusting for pre-military offending behaviour [9,11,31]. Furthermore, mental disorders such as PTSD, as well as alcohol misuse, have been shown to be risk factors for violence, and more general offending behaviour, among military personnel [9,11,31,33]. It is likely that a combination of these factors contributes to the overall increase in violence among veterans.

An association between veteran status and motoring offences was also found. This is consistent with evidence that road-traffic accidents are prevalent among both UK and US military personnel, and that deployment increases the likelihood of risky driving [3638]. This may reflect a general trend towards increased risk-taking behaviour observed among military personal following return from deployment and after leaving service [39].

We observed a crude association between veteran status and sex offending. However, this association was no longer statistically significant after adjusting for socio-demographic differences. Previous studies found age-adjusted significant associations between veteran status and sex offending in UK prisons and probation services [3,6]. Our study suggests that further adjustment for differences in additional socio-demographic variables such as gender, ethnicity, and employment status accounts for this association.

Associations were shown between veteran status and anxiety disorders, dementia and multiple mental health problems. There is a wealth of evidence that links military service (in particular, combat exposure) with PTSD [4042]. It is likely that a proportion of veterans classified as suffering with an anxiety disorder may have been suffering from PTSD symptoms, but we were unable to examine this further due to the crude categorisation of mental disorders in the dataset. Higher prevalence rates of common mental disorders, such as depression and anxiety, among Armed Forces personnel may be due to (or exacerbated by) the increased likelihood of exposure to stressors within their service roles [43], and/or the psychosocial stress associated with transition out of the military and back into civilian life [44,45]. Little is known about the prevalence of dementia in Armed Forces veterans, but a recent review suggests that veterans are at greater risk of dementia than non-veterans, perhaps due to their increased risk of other mental illnesses such as PTSD and depression [46]. We also found that non-veterans were more likely than veterans to report schizophrenia, ADHD, substance use, learning- and social/communication-difficulties. Military selection processes are likely to exclude individuals with serious mental illnesses, and the presence of these conditions would prevent someone from enlisting. This, along with the implementation of routine drug testing in the Armed Forces may account for these differences.

Veteran status was associated with hazardous (but not dependent) drinking and physical health problems. Research shows that rates of hazardous and dependent drinking among UK Armed Forces personnel are higher than among those the general population, irrespective of gender [47]. Some physical health problems among veterans are likely to be related to military service, but we were unable to establish this from the data.

Within the veteran sample, factors associated with violence against the person, acquisitive, and motoring offending were also examined. Violence against the person offences were associated with a recording of anxiety disorders (compared to other non-violent and non-sexual offences). There is a well-established link between anxiety and violence/aggression [48,49], which may be explained by deficits in emotion regulation [50,51]. Given that the veterans in the sample had a high prevalence of anxiety disorders, it is possible that the offending behaviour stems from higher levels of emotion dysregulation in veterans with anxiety. Also, PTSD was classed as an ‘anxiety disorder’ in this study’s database, which may have additionally contributed to the finding of the link between anxiety disorder and violent offending. Irritability, aggression and reckless behaviour are core symptoms of PTSD in the DSM 5 [52], and PTSD has been shown to be strongly associated with violent offending among military personnel [11,33]. Perhaps surprisingly, we did not find that alcohol misuse was associated with interpersonal violence among veterans. However, we were not comparing violent offending with non-offending in this sample. This finding tells us that veterans who misuse alcohol were no more likely to commit an offence of violence against the person than an acquisitive, or other non-violent, offence.

Acquisitive and motoring offences were associated with substance misuse and alcohol misuse, respectively (compared to all other types of offences). There is a well-established link between substance misuse and crime in general, and this is consistent across different types of substances and different types of offending [53]. Although the specific link between substance misuse and acquisitive offending has not been studied in veteran populations, our finding is unsurprising. The presence of alcohol misuse was a risk factor for motoring offences in veterans, after controlling for socio-demographic variables. This finding, again, is unsurprising given the link between alcohol consumption and risky driving behaviours [54,55].

Strengths and limitations

A major strength of our study was the large sample size, especially of the non-veteran group, which was likely to be representative of L&D service-users. This allowed us to account for the effects of potentially confounding socio-demographic factors, which would not have been possible using a smaller sample. We were also able to directly compare veterans and non-veterans that were members of the same population–i.e. vulnerable individuals in the CJS. This is a major advantage, as any differences between them are unlikely to be biased due to different sampling methods. Furthermore, given that individuals are referred to L&D services from a range of settings, our data include individuals who had committed (or were suspected of having committed) a range of offences: from summary offences to murder and manslaughter. This increases the generalisability of our findings.

Despite these strengths, the results of our study should be interpreted in the light of a number of limitations. First, there was a significant amount of missing data. For example, 20% of cases had no veteran status, suggesting that the question is not always asked by L&D service practitioners. However, this level of missingness is not unusual among administrative datasets [56]. Furthermore, the sample size was large enough to minimise the effect of these missing data on statistical power, and enabled us to adjust for socio-demographic variables. Second, it is likely that there were arbitrary associations in the data within each L&D site due to: a) the variation in screening tools between, but not within, each site, which may have resulted in common method bias [57]; and b) common recording practices within (but not between) each site. We attempted to adjust for this clustering effect in our main analyses using methods commonly used in similar studies [26]. In addition, we were still able to demonstrate significant differences (both statistically and clinically) between veterans and non-veterans who were members of the same population (i.e. vulnerable individuals in the CJS). Third, we were unable to reliably identify repeat service-users within the database. However, our sensitivity analysis suggests that the differences we found between veterans and non-veterans are unlikely to have resulted from this issue. Fourth, the definition used by L&D services to classify whether the individual is a member of the Armed Forces is someone who has served at least one day in the UK Armed Forces. Such a definition includes individuals who may not have completed their basic training. It is likely that the number of such individuals in this dataset would be small, but we were unable to identify them. However, these individuals are currently eligible for NHS veteran community mental health services and so their inclusion in the present study is valid. Fifth, the offence types that we examined within our dataset reflect the offence-type that the individual was charged with, rather than convicted of. Similar to criminal justice systems in other parts of the world (such as the US), in the UK a charge and a conviction may not necessarily reflect the same offence (due to factors such as plea-bargaining or judicial prerogative). However, existing research suggests that arrest records (rather than conviction records) may be more reflective of the actual crime committed by the individual, as they are less likely to be affected by bureaucratic processes within the CJS [58].

Implications

This study has highlighted the utility of using secondary routinely collected data from services engaged with offenders early in their CJS journey in order to identify those with specific needs and allow early intervention. We have identified that, among L&D service users, those who have served in the Armed Forces have offending, welfare, physical health, mental health and substance misuse needs that differ from general population offenders. In some jurisdictions in the United States of America such is the recognition of these differences in need that special veteran courts operate to divert some veterans into justice reform projects for veterans [5961]. The creation of such courts is unlikely to be viable in countries, such as the United Kingdom, which have smaller Armed Forces. More importantly, any suggestion that former service personnel should be tried in different courts from their civilian counterparts raises the spectre of the creation of other special courts for other public servants in stressful roles, such as the police.

In other jurisdictions and countries, such as the UK, where veteran courts are not viable, the differences observed between veterans and non-veterans highlight the need for workforce training across the CJS to improve the identification of veterans and understanding of their needs within the CJS. The different pattern of mental health needs among veterans, such as the higher rate of anxiety disorders, and the links between mental disorders and offending behaviour, especially the link between anxiety disorder and interpersonal violence, emphasises the need for improved availability of psychological therapies in general in prison and the need for staff with an understanding of the impact of military service and life after the military on mental health and risk of offending. This may be especially important, given research showing that veterans tend to improve less from standard PTSD treatment approaches than non-veterans [62]. Thus, in some areas, specialist veteran in-reach services in the CJS may be a viable solution. However, where such services are not an option, an integrated approach to the delivery of mental health, substance-misuse and welfare services should be the goal, with veterans’ champions co-ordinating care to ensure the holistic needs of veterans are met and to ultimately reduce the risk of re-offending. Our results also highlight the importance of improving the assessment and treatment of trauma-related mental health problems, which would not only benefit veterans, but also the general offender population.

Supporting information

S1 Table. Association of socio-demographic variables with violence against the person offences within the veteran sample.

https://doi.org/10.1371/journal.pone.0207282.s001

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S2 Table. Association of mental health needs with violence against the person offences within the veteran sample.

https://doi.org/10.1371/journal.pone.0207282.s002

(XLSX)

S3 Table. Association of socio-demographics with acquisitive offences in the veteran sample.

https://doi.org/10.1371/journal.pone.0207282.s003

(XLSX)

S4 Table. Association of mental health needs with acquisitive offences in the veteran sample.

https://doi.org/10.1371/journal.pone.0207282.s004

(XLSX)

S5 Table. Association of socio-demographic variables with motoring offences within the veteran sample.

https://doi.org/10.1371/journal.pone.0207282.s005

(XLSX)

S6 Table. Association of mental health needs with motoring offences within the veteran sample.

https://doi.org/10.1371/journal.pone.0207282.s006

(XLSX)

Acknowledgments

We thank all of the L&D service practitioners who were involved in the data entry related to this research.

References

  1. 1. Ashcroft M. The Veterans’ Transition Review. Biteback Publishing; 2014.
  2. 2. MacManus D, Jones N, Wessely S, Fear NT, Jones E, Greenberg N. The mental health of the UK Armed Forces in the 21st century: resilience in the face of adversity. J R Army Med Corps. 2014;0:1–6.
  3. 3. DASA. Estimating the proportion of prisoners in England and Wales who are ex-Armed Forces–further analysis. London; 2010.
  4. 4. HMIP. People in prison: Ex-service personnel. London: HMIP; 2014.
  5. 5. Treadwell J. Counterblast: More than casualties of war?: Ex-military personnel in the Criminal Justice System. Howard J Crim Justice. 2010;49:73–7.
  6. 6. DASA. Estimating the proportion of offenders supervised by Probation Trusts in England and Wales who are ex-Armed Forces. London; 2011.
  7. 7. Phillips S. Former members of the Armed Forces and the Criminal Justice System. A review on behalf of the Secretary of State for Justice. London; 2014.
  8. 8. MacManus D, Wood N. The ex-armed forces offender and the UK criminal justice system. In: Hughes JH, editor. Mil Veteran Psychol Heal Soc Care Contemp Issues. Oxford: Routledge; 2017. p. 62–80.
  9. 9. MacManus D, Dean K, Al Bakir M, Iversen AC, Hull L, Fahy T, et al. Violent behaviour in UK military personnel returning home after deployment. Psychol Med. 2012;42:1663–73. pmid:22115074
  10. 10. Simonoff E, Elander J, Holmshaw J, Pickles A, Murray R, Rutter M, et al. Predictors of antisocial personality: Continuities from childhood to adult life. Br J Psychiatry. 2004;184:118–27. pmid:14754823
  11. 11. MacManus D, Dean K, Jones M, Rona RJ, Greenberg N, Hull L, et al. Violent offending by UK military personnel deployed to Iraq and Afghanistan: a data linkage cohort study. Lancet. 2013;381:907–917. pmid:23499041
  12. 12. Kwan J, Jones M, Hull L, Wessely S, Fear N, MacManus D. Violent behavior among military reservists: Violence Among Reservists. Aggress Behav. 2017;43:273–80. pmid:27775160
  13. 13. Elbogen EB, Johnson SC, Wagner HR, Newton VM, Timko C, Vasterling JJ, et al. Protective factors and risk modification of violence in Iraq and Afghanistan war veterans. J Clin Psychiatry. 2012;73:e767–73. pmid:22795217
  14. 14. Elbogen EB, Johnson SC, Newton VM, Straits-Troster K, Vasterling JJ, Wagner HR, et al. Criminal justice involvement, trauma, and negative affect in Iraq and Afghanistan war era veterans. J Consult Clin Psychol. 2012;80:1097–102. pmid:23025247
  15. 15. MacManus D, Burdett H, Jones M, Hull L, Wessely S, Fear NT. Risk and protective factors for offending among UK military personnel after leaving service. In press.
  16. 16. Elbogen EB, Johnson SC, Newton VM, Timko C, Vasterling JJ, Van Male LM, et al. Protective mechanisms and prevention of violence and aggression in veterans. Psychol Serv. 2014;11:220–8. pmid:24512537
  17. 17. Van Dyke K, Orrick EA. An examination of the influence of veteran status on offense type among an inmate sample. Am J Crim Justice. 2017;42:426–42.
  18. 18. Babor T, Higgins-Biddle JC, Saunders JB, Monteiro MG. The Alcohol Use Disorders Identification Test: Guidelines for use in primary care. Geneva World Heal Organ. 2001;1–40.
  19. 19. Mueser KT, Drake RE, Clark RE, McHugo GJ, Mercer-McFadden C, Ackerson TH. Evaluating substance abuse in persons with severe mental illness. Cambridge, MA: Human Services Research Institute; 1995.
  20. 20. McKenzie K, Paxton D. Promoting access to services: the development of a new screening tool. Learn Disabil Pract. 2006;9:17–21.
  21. 21. Hayes SC. Early intervention or early incarceration? Using a screening test for intellectual disability in the criminal justice system. J Appl Res Intellect Disabil. 2002;15:120–8.
  22. 22. Baron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E. The autism-spectrum quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J Autism Dev Disord. 2001;31:5–17. pmid:11439754
  23. 23. StataCorp. Stata Statistical Software: Release 14. College Station, TX: StatCorp LP; 2015.
  24. 24. Williams RL. A note on robust variance estimation for cluster-correlated data. Biometrics. 2000;56:645–6. pmid:10877330
  25. 25. Rogers WH. Regression standard errors in clustered samples. Stata Tech Bull. 1993;13:19–23.
  26. 26. Huang FL. Alternatives to Multilevel Modeling for the Analysis of Clustered Data. J Exp Educ. 2016;84:175–96.
  27. 27. Tsai J, Rosenheck RA, J. Kasprow W, McGuire JF. Risk of incarceration and other characteristics of Iraq and Afghanistan era veterans in state and federal prisons. Psychiatr Serv. 2013;64:36–43. pmid:23280456
  28. 28. Brooke EJ, Gau JM. Military Service and Lifetime Arrests: Examining the Effects of the Total Military Experience on Arrests in a Sample of Prison Inmates. Crim Justice Policy Rev. 2018;29:24–44.
  29. 29. Greenberg GA, Rosenheck RA, Desai RA. Risk of incarceration among male veterans and nonveterans: are veterans of the all volunteer force at greater risk? Armed Forces Soc. 2007;33:337–50.
  30. 30. Booth-Kewley S, Highfill-McRoy RM, Larson GE, Garland CF. Psychosocial predictors of military misconduct. J Nerv Ment Dis. 2010;198:91–98. pmid:20145482
  31. 31. Elbogen EB, Johnson SC, Newton VM, Fuller S, Wagner HR, Beckham JC. Self-report and longitudinal predictors of violence in Iraq and Afghanistan war era veterans. J Nerv Ment Dis. 2013;201:872–6. pmid:24080674
  32. 32. MacManus D, Rona R, Dickson H, Somaini G, Fear N, Wessely S. Aggressive and violent behavior among military personnel deployed to Iraq and Afghanistan: prevalence and link with deployment and combat exposure. Epidemiol Rev. 2015;37:196–212. pmid:25613552
  33. 33. Elbogen EB, Johnson SC, Wagner HR, Sullivan C, Taft CT, Beckham JC. Violent behaviour and post-traumatic stress disorder in US Iraq and Afghanistan veterans. Br J Psychiatry. 2014;204:368–75. pmid:24578444
  34. 34. MacManus D, Dean K, Iversen AC, Hull L, Jones N, Fahy T, et al. Impact of pre-enlistment antisocial behaviour on behavioural outcomes among UK military personnel. Soc Psychiatry Psychiatr Epidemiol. 2012;47:1353–8. pmid:22038568
  35. 35. MacManus D, Wessely S. Why do some ex-armed forces personnel end up in prison? BMJ. 2011;342:d3898–d3898. pmid:21700653
  36. 36. Sheriff RJS, Forbes HJ, Wessely SC, Greenberg N, Jones N, Fertout M, et al. Risky driving among UK regular armed forces personnel: changes over time. BMJ Open. 2015;5:e008434. pmid:26399573
  37. 37. Fear NT, Iversen AC, Chatterjee A, Jones M, Greenberg N, Hull L, et al. Risky driving among regular armed forces personnel from the United Kingdom. Am J Prev Med. 2008;35:230–6. pmid:18617356
  38. 38. Lincoln AE, Hooper TI, Kang HK, DeBakey SF, Cowan DN, Gackstetter GD. Motor vehicle fatalities among Gulf War era veterans: Characteristics, mechanisms, and circumstances. Traffic Inj Prev. 2006;7:31–7. pmid:16484030
  39. 39. Killgore WDS, Cotting DI, Thomas JL, Cox AL, McGurk D, Vo AH, et al. Post-combat invincibility: Violent combat experiences are associated with increased risk-taking propensity following deployment. J Psychiatr Res. 2008;42:1112–21. pmid:18291419
  40. 40. Iversen AC, van Staden L, Hughes JH, Browne T, Hull L, Hall J, et al. The prevalence of common mental disorders and PTSD in the UK military: using data from a clinical interview-based study. BMC Psychiatry. 2009;9:68. pmid:19878538
  41. 41. Fulton JJ, Calhoun PS, Wagner HR, Schry AR, Hair LP, Feeling N, et al. The prevalence of posttraumatic stress disorder in Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) Veterans: A meta-analysis. J Anxiety Disord. Elsevier Ltd; 2015;31:98–107. pmid:25768399
  42. 42. Richardson LK, Frueh BC, Acierno R. Prevalence estimates of combat-related post-traumatic stress disorder: critical review. Aust N Z J Psychiatry. 2010;44:4–19. pmid:20073563
  43. 43. Goodwin L, Wessely S, Hotopf M, Jones M, Greenberg N, Rona RJ, et al. Are common mental disorders more prevalent in the UK serving military compared to the general working population? Psychol Med. 2015;45:1881–91. pmid:25602942
  44. 44. Hatch SL, Harvey SB, Dandeker C, Burdett H, Greenberg N, Fear NT, et al. Life in and after the Armed Forces: Social networks and mental health in the UK military. Sociol Heal Illn. 2013;35:1045–64.
  45. 45. Kapur N, While D, Blatchley N, Bray I, Harrison K. Suicide after leaving the UK Armed Forces—A cohort study. PLoS Med. 2009;6:e100002.
  46. 46. Rafferty LA, Cawkill PE, Stevelink SAM, Greenberg K, Greenberg N. Dementia, post-traumatic stress disorder and major depressive disorder: a review of the mental health risk factors for dementia in the military veteran population. Psychol Med. 2018;1–10.
  47. 47. Fear NT, Iversen A, Meltzer H, Workman L, Hull L, Greenberg N, et al. Patterns of drinking in the UK Armed Forces. Addiction. 2007;102:1749–59. pmid:17935583
  48. 48. Neumann ID, Veenema AH, Beiderbeck DI. Aggression and anxiety: social context and neurobiological links. Front Behav Neurosci. 2010;4:12. pmid:20407578
  49. 49. Hodgins S, De Brito S, Chhabra P, Côté G. Anxiety disorders among offenders with antisocial personality disorders: a distinct subtype? Can J Psychiatry. 2010;55:784–91. pmid:21172099
  50. 50. Roberton T, Daffern M, Bucks RS. Emotion regulation and aggression. Aggress Violent Behav. Elsevier Ltd; 2012;17:72–82.
  51. 51. Miles SR, Sharp C, Tharp AT, Stanford MS, Stanley M, Thompson KE, et al. Emotion dysregulation as an underlying mechanism of impulsive aggression: Reviewing empirical data to inform treatments for veterans who perpetrate violence. Aggress Violent Behav. 2017;34:147–53.
  52. 52. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. Washington, D.C.: Author; 2013.
  53. 53. Bennett T, Holloway K, Farrington D. The statistical association between drug misuse and crime: A meta-analysis. Aggress Violent Behav. 2008;13:107–18.
  54. 54. Van Dyke NA, Fillmore MT. Laboratory analysis of risky driving at 0.05% and 0.08% blood alcohol concentration. Drug Alcohol Depend. Elsevier; 2017;175:127–32.
  55. 55. Fillmore MT, Blackburn JS, Harrison ELR. Acute disinhibiting effects of alcohol as a factor in risky driving behavior. Drug Alcohol Depend. 2008;95:97–106. pmid:18325693
  56. 56. Peugh JL, Enders CK. Missing data in educational research: A review of reporting practices and suggestions for improvement. Rev Educ Res. 2004;74:525–56.
  57. 57. Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J Appl Psychol. 2003;88:879–903. pmid:14516251
  58. 58. Rosellini AJ, Monahan J, Street AE, Heeringa SG, Hill ED, Petukhova M, et al. Predicting non-familial major physical violent crime perpetration in the U.S. Army from administrative data. Psychol. Med. 2016. p. 303–16. pmid:26436603
  59. 59. Pandiani JA, Ochs WR, Pomerantz AS. Criminal justice involvement of armed forces veterans in two systems of care. Psychiatr Serv. Am Psychiatric Assoc; 2010;61:835–7. pmid:20675844
  60. 60. Cavanaugh JM. Helping those who serve: Veterans treatment courts foster rehabilitation and reduce recidivism for offending combat veterans. New Eng L Rev. HeinOnline; 2010;45:463.
  61. 61. Tsai J, Flatley B, Kasprow WJ, Clark S, Finlay A. Diversion of Veterans With Criminal Justice Involvement to Treatment Courts: Participant Characteristics and Outcomes. Psychiatr Serv. 2017;68:375–83. pmid:27903139
  62. 62. Steenkamp MM, Litz BT, Hoge CW, Marmar CR. Psychotherapy for military-related PTSD: A review of randomized clinical trials. JAMA—J Am Med Assoc. 2015;314:489–500.