Notes
Article history
The research reported in this issue of the journal was funded by PGfAR as project number RP-PG-0407-10500. The contractual start date was in July 2008. The final report began editorial review in July 2014 and was accepted for publication in June 2015. As the funder, the PGfAR programme agreed the research questions and study designs in advance with the investigators. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The PGfAR editors and production house have tried to ensure the accuracy of the authors’ report and would like to thank the reviewers for their constructive comments on the final report document. However, they do not accept liability for damages or losses arising from material published in this report.
Declared competing interests of authors
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© Queen’s Printer and Controller of HMSO 2016. This work was produced by Coid et al. under the terms of a commissioning contract issued by the Secretary of State for Health. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising. Applications for commercial reproduction should be addressed to: NIHR Journals Library, National Institute for Health Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK.
Chapter 1 Introduction
Violence-related morbidity is a key public health problem1 resulting in major concern among the public and policy-makers in the UK. Interventions to prevent violence are no longer the sole responsibility of criminal justice agencies2 and mental health services have become increasingly involved in initiatives to reduce violence. However, prevention in the field of mental health is underdeveloped and almost exclusively operates at secondary and tertiary levels. This means that mental health professionals are restricted to secondary preventative measures, such as early detection of violence risk with the aim of prompt intervention to prevent violence occurring among service users, or tertiary prevention, including measures aimed to reduce the impact on the public of future violence from those who have already been identified as a potential risk on the basis of their previous behaviour. At present, tertiary-level strategies focus on reducing the impact of identified risk factors, including psychiatric morbidity, usually after violence has occurred.
Violence prevention is not recognised as a primary function in all mental health services. Furthermore, it tends to be just one of several goals even within secure mental health services. This means that the primary prevention of violence (which consists of actions and measures aimed at inhibiting the emergence of environmental, economic, social and behavioural conditions, cultural patterns of living, etc., known to increase the risk of violence) is not yet perceived as a core focus of mental health services. Public mental health equivalents of primary prevention through personal and communal efforts, such as enhancing nutritional status, immunising against communicable diseases and eliminating environmental risks, have not yet been developed. Risk assessment, which is designed to facilitate risk management in mental health and the criminal justice system, is of increasing importance but represents the equivalent of secondary and tertiary levels of prevention. By implication, risk management by mental health professionals for violence involves targeted, rather than population, interventions with individuals who are recognised as being of high risk. 3
Compton et al. 4 have argued that the population approach to prevention can be applied in mental health. Universal preventative interventions or population interventions target a whole population or the general public. 5 Such interventions benefit everyone in the population, regardless of their risk for violence. These might include public service announcements or media campaigns to prevent substance abuse, legislation to increase the legal drinking age or more serious penalties for violent behaviour and carrying weapons. Selective preventative interventions target individuals or subgroups of the population whose risk of violence is significantly higher than average. A high-risk group may be identified by psychological, biological or social risk factors. An intervention can include lifestyle modification to avoid situations in which individuals encounter or become involved in violence or, as indicated by our programme, an intervention for boys who witness violence in the family home (see Chapter 7, Study 1).
Indicated preventative interventions target particularly high-risk individuals – those with risk factors, or a condition, that identify them as being at high risk of future violence. At the present time, much of the debate around risk assessment in mental health and the criminal justice system has focused on the accuracy of identifying high-risk individuals. This is primarily because of the implications for these individuals if judged to be at high risk. Interventions available for those who are considered to be at exceptionally high risk, for example psychopathy, are often very limited. Interventions can include prolonged detention, more severe sentencing in court and detention and treatment in hospital against an individual’s will.
Development of risk assessment has been led primarily by clinical psychologists over the past 30 years. As the law has turned to behavioural and medical sciences to improve accuracy in the assessment and management of violence, specialist instruments (or ‘tools’) have been developed for the prediction and management of certain kinds of serious violence and criminal offending. 6 It has been estimated that > 120 risk assessment instruments have been developed and promoted for use in mental health services and the criminal justice system, with competing claims of superiority with regard to accuracy. 7 However, our programme has questioned the superiority of the predictive accuracy of any one tool over another. 8 Furthermore, few of these instruments lead easily to the second and essential component of a risk assessment – a plan of risk management. Bridging to risk management is described as the key component. 9 However, few currently available risk assessment tools either link to or incorporate risk management strategies.
Changes to the original aims of the programme
The theoretical basis of the research programme underwent major changes over the 5-year period. Unexpected findings conflicted with established views of how risk should be assessed, as described in the international literature. These had determined the basis of the original application and its aims. By the time the programme began, risk assessment for violence had become divided into two main approaches: actuarial risk assessment (ARA) and structured professional judgement (SPJ). Adherents to these approaches had become increasingly in opposition to each other. Our original application was largely committed to the actuarial approach to develop our new instruments during the programme. ARA instruments provide numerical probabilities of future violence, at different subsequent time intervals from a baseline measurement. This is a predictive method. Adherents to the alternative approach, SPJ, have argued that an actuarial approach is inappropriate for risk assessment because probabilities based on the group average method do not apply to the individual being assessed. 10 Assessors are encouraged instead to rate individual risk items according to their presence or absence and then formulate their risk-level rating according to a global, clinical understanding of the risk posed by the individual, followed by possible scenarios that could result in violence that might occur in the future.
By the time the programme started, the UK criminal justice system and the NHS mental health systems had proceeded in opposite directions. Risk assessment within the criminal justice system is currently based on a series of actuarial measures developed from large data sets of offenders. Ratings typically determine level of security and intensity of supervision and are provided to sentencers in courts to assist with sentencing or to parole boards to help determine parole. These are administered by offender managers (probation and prison staff). This method provides a highly efficient and economic method of assessing risk and can be carried out with minimal training. Probabilities of an individual’s future offending can be determined using computerised data derived from routine ratings by Ministry of Justice staff. However, our programme increasingly began to question the accuracy of these measures, corresponding to doubt expressed by previous investigators,10,11 particularly the high percentage of individuals incorrectly classified as at risk of further offending or not at risk.
Mental health services had meanwhile adopted SPJ, specifically the use of the Historical, Clinical, Risk Management-20 (HCR-20). 12 The authors of this instrument are Canadian. Over the time span of the programme, intensive training for NHS staff as well as staff in other European countries had begun. Local initiatives for risk assessment previously introduced by NHS trusts were replaced by SPJ, which became the risk assessment of choice in mental health services. For some NHS services, completion of the HCR-20 for each patient had become essential to receive funding from commissioners.
To carry out a risk assessment such as the HCR-20, staff require intensive training. Completion of the instrument can take over an hour. Clinical teams in mental health services typically complete the assessment together, with a member of the team filling in the forms. These are then filed as a hard copy in case notes or entered in electronic format. Different services have opted for different time spans over which the SPJ should be repeated, although in most cases this is on a single occasion during hospital admission. There is little indication that SPJ continues routinely after discharge. This is when service users are most likely to encounter new or previously demonstrated risk factors and when intervention is most urgently required if there is an indication of impending violence.
Structured professional judgement also provides no clinical advice on how, when or indeed whether or not to intervene; instead, it considers whether or not the service user is a risk and encourages the rater to imagine what situations might occur to increase risk. The strategy of management is then for the clinician to determine.
The costs of using SPJ methods include fees for training. European trainers now increasingly run courses, with less reliance on employing North American teachers. More recently, however, new versions of SPJ have become increasingly expensive and it is necessary to buy manuals from North America. Trainers in North America and Europe have also developed their own commercial interests. There is therefore a need for the NHS to develop new approaches to risk assessment and develop its own products. This became a key aim of our research by the end of the programme with our development of new Bayesian networks.
The programme of research therefore progressed through a series of phases. It initially became necessary to ‘deconstruct’ earlier approaches to risk assessment that had been applied within the actuarial approach, but also within SPJ. At an early stage we discovered that the statistical model that we had chosen to develop our risk assessment instrument, and which had appeared to show considerable promise before we started, conveyed no benefits whatsoever. Furthermore, it was likely to result in an instrument with poorer accuracy than if we had used conventional statistical methods. This corresponded to the disappointing findings from the US MacArthur risk study,13–15 the largest and most expensive ever conducted, which had used a similar statistical method. This US programme had ultimately failed in its aim to develop a new method of risk assessment with superior accuracy for patients discharged from psychiatric hospitals in the USA. 13–15 We had intended to develop a similar method of risk assessment in our programme and compare it with the McArthur Classification of Violence Risk (COVR) instrument. 13
The reasons for this failure are now entirely clear from our research. As we learned more about the shortcomings of risk assessment, particularly the actuarial approach, it became necessary to substantially revise our approach. Our original aims and objectives for the programme therefore changed. At the same time, we also became increasingly sceptical about the SPJ approach. SPJ had evolved during the early stages of our programme, exemplified in the change from the HCR-20 version 2 (HCR-20v2)16 to the HCR-20 version 3 (HCR-20v3),17 which we had agreed to validate in our programme with the authors of the instrument. Whereas proponents of the SPJ approach were highly critical of the rival actuarial method of adding scores from risk items, this method had previously been widely used in the rating of the HCR-20v2. It was now prohibited for clinical use in the new third version and assessors were instructed to make overall correlations of ‘high’, ‘medium’ or ‘low’ future risk based on their global perception following the assessment. Nevertheless, all empirical research using SPJ continues to rely on producing numerical scores of risk based on individual items. Validation of SPJ instruments therefore continues to depend on actuarial prediction methods, most commonly using the area under the receiver operating characteristic (ROC) curve (AUC) statistic, to determine their accuracy of prediction of future violence. Although we have applied this method in our programme, we have also investigated its considerable limitations and proposed alternatives.
One additional limitation became apparent from our research: the HCR-20 and other SPJ instruments are ‘checklists’. They do not allow for the multidimensional approach that is necessary for a comprehensive risk assessment. They depend on a ‘compartmentalised’ assessment of a predetermined number of areas. These are fixed and not determined by an individual’s previous longitudinal history. The HCR-20v3 has been improved and considerably expanded in an attempt to incorporate more information on risk and encourages assessors to use their own judgement and identify additional risks. However, the included items are not covered in a manner similar to clinical history taking in mental health services. For a clinical assessment, individual components such as criminal history require a greater depth of understanding if the links are to be made between a previous history of antisocial behaviour and associated risk and predictive factors. The HCR-20 does not capture the potential synergistic effects of these different components and ultimately relies on clinical experience and the expertise of the clinician rater. Finally, SPJ does not impel the rater to intervene when a risk factor is clearly present or indicate which factors should be targeted for intervention on the basis of an established causal link between the factor and violent outcome, and these limitations are the motives for moving from a risk assessment to effective risk management. They became our primary aim by the end of our programme.
Finally, and most importantly, there is currently no evidence that SPJ can predict violence more accurately than an ARA instrument. 8 Furthermore, there is currently no evidence that either an ARA instrument or an assessment based on SPJ can prevent violence. The only randomised controlled trial (RCT) of a SPJ instrument, the Short-Term Assessment of Risk and Treatability (START), failed to demonstrate any improvement in clinical outcome or violence reduction compared with management as usual. 18 Although our programme provides a third alternative to ARA instruments and SPJ, a RCT is clearly the next phase of research required; it will be necessary to test our Bayesian models in the future against current clinical practice.
A theoretical model of risk pathway to violence
This report presents a new approach to risk assessment and risk management (Figure 1) that could be incorporated into clinical practice. It is based on a model of assessment that we have empirically tested, but is also derived from clinical experience. It is based on a longitudinal approach that aims to capture the evolution of risk over time when an offender is released from custody or a service user is discharged from hospital. Clinical risk assessment of an individual would proceed through each of the five stages in Figure 1. This would be based on previous behaviour and a full assessment of an individual’s previous history, including current circumstances and reason for the assessment, detailed assessment of previous and more recent violent and criminal behaviour, family history and developmental history from childhood to the present day. Although Figure 1 shows that the first stage of the assessment is to make a diagnosis, clinicians would point out that this is usually completed towards the end of a clinical assessment. It would include assessment of current and previous mental state to attain a formal diagnosis of mental disorder, including personality disorder and history of substance misuse. Figure 1 is therefore the basis of a risk formulation. This would follow a full clinical assessment. It also forms the basis of our programme of research.
FIGURE 1.
Theoretical model of risk pathway to violence.
Figure 1 therefore begins with the establishment of a psychiatric diagnosis at the outset together with the level of static risk for future violence, based on actuarial measures. We have already referred to the limitations of the actuarial approach but it is necessary to demonstrate these in this programme report in coming to this conclusion. We believe that an actuarial measure of risk has some value but this is limited. We shall explain the limits and the importance of combining actuarial measures with dynamic risk assessment. A clinician may wish to know the score of risk based on previous offending and this may result in a more in-depth profiling of an individual’s criminal history, but should not be the basis of major decisions such as release from hospital or prison. It is also of some limited value when screening offenders to indicate when an in-depth clinical assessment is required. However, when combined with dynamic factors, a static or actuarial measure of risk can provide a more accurate assessment and indicate dynamic risk factors that will be targeted for intervention.
Stage 3 requires an assessment of ongoing dynamic risk factors, which are changeable and should be identified as targets for future intervention. In our previous research we identified the importance of protective factors and the interaction that can occur between protective factors and risk factors. 19 However, protective factors were not an aim of the programme and are not described in detail in this report.
In stage 4, the individual may encounter acute risk factors that have a direct influence on subsequent behaviour, for example acute intoxication or involvement in a group or gang, which have a more immediate bearing on violent outcome.
At stage 5, certain trigger factors, which have an immediate and causal effect on the violent outcome, may be encountered. Alternatively, the violence may have been planned for some time, although a sudden triggering factor can occur in certain cases when planning has been present.
In developing this model we have shifted considerably from our original goal, which was to develop predictive measures. Our programme of research changed to the identification of causal risk factors because only causal factors should be targets for intervention. A risk factor may be highly predictive of future violence but, if it is not causal, attempts to modify it will not prevent future violence from occurring. In contrast, we have observed that causal risk factors may have no predictive ability in estimating the probability of future violence. This is particularly the case for a dynamic risk factor that shows fluctuations of intensity, such as the presence of anger as a result of delusions. 20,21
Aim
Our overall aim was to carry out a programme of linked research studies aimed at improving the quality of clinical risk management of individuals identified as being at high risk of harm to the public.
Section A of our report examines both static risk factors and psychiatric diagnosis (relating to stages 1 and 2 of our theoretical model in Figure 1). We shall show that risk of violence in terms of cross-sectional associations at the population level differs according to both diagnostic category and demographic factors.
Section B of our report describes the development of a risk assessment tool for patients presenting to general psychiatric services based on a first-episode cohort of patients with psychoses. This section describes a model based on multilevel modelling, a sophisticated statistical approach to multiple measures over a prolonged period. This is a prototype for further development. Section B covers stages 1–4 of the model.
Section C of our report describes the Validation of new Risk Assessment Instruments for Use with Patients Discharged from Medium Secure Services (VoRAMSS) study, a prospective follow-up of patients discharged from medium secure units (MSUs) across England. In this section, which covers stages 1–4 of the model, we test the accuracy of prediction of violent behaviour of standardised risk assessment instruments, ARA instruments and SPJ over the 12 months following discharge. We also validate the Medium Security Recidivism Assessment Guide (MSRAG),22 an actuarial instrument that we developed previously. We shall demonstrate that all ARA instruments and SPJ have shortcomings if validated using predictive methods and that dynamic factors are essential measures because they directly influence the violent behaviour. Nevertheless, we describe a new way of using ARA instruments in conjunction with dynamic measures that can improve accuracy.
Section D describes the development of four new ARA instruments for violent, robbery, drug-related and acquisitive offending. We have developed a computerised version of each as well as a version that can be used by clinicians with pencil and paper. Although ARA instruments clearly have their limitations, we show that combining them with dynamic factors shows a highly promising method that can be adapted and used in clinical practice. We then developed a dynamic risk assessment instrument to combine with these ARA instruments and validated it using a very large data set provided to us by the National Offender Management Service (NOMS). In the criminal justice system, offender managers use ARA instruments because they are economical, are less time-consuming and do not require intensive training. It is not possible to routinely use SPJ because of the number of offenders who must be assessed and the consequent size of caseloads. The instruments that we have developed are therefore of primary usefulness to the work of criminal justice personnel, especially probation officers. Because probation officers routinely complete Offender Assessment System (OASys) ratings on their clients that are computerised, our model can now be adapted for routine use for improved risk assessment, with important implications for risk management.
Section E describes the development of a Bayesian network to assess risk and identify the key dynamic risk factors for preventative interventions to guide risk management. Bayesian networks were chosen for this section of the programme because they are used to identify factors that are causal. They can provide actuarial measures of risk as well, but are better suited to our main aim of establishing causal associations. This final and most important part of our programme has established the basis for further development in the field of risk assessment and management. We have developed models that have operated successfully and have been used in their preliminary prototypic form by clinicians. They are at the stage of development before programming of a computerised application (app) for use by a clinician on a tablet in the field. Following this stage, the models will be ready for comparison with standard SPJ in a RCT to assess their effectiveness for violence prevention.
Table 1 summarises the studies, populations and outcomes presented in this report.
| General population | Adult psychiatric population | Adult forensic population | Prisoners | |
|---|---|---|---|---|
| Study | Psychiatric morbidity among adults living in private households in England, Wales and Scotland (n = 8880) | First-episode psychosis: baseline (n = 409), follow-up (n = 389) | VoRAMSS study: baseline (n = 409), 6-month follow-up (n = 387), 12-month follow-up (n = 344) | PCS: pre release (n = 1717), post release (n = 1004) |
| APMS (n = 7403) | ||||
| Men’s Modern Lifestyle Survey: main (n = 3247), ethnic minority booster (n = 1540), low social class booster (n = 1002), Hackney, London, booster (n = 883), Glasgow East booster (n = 789) | NOMS (n = 53,800) | |||
| Location in report | Section A | Section B | Sections C and E | Section D |
| Outcome | Identification of risk factors for violence in the general population | Development of sex-specific static and dynamic instruments for the assessment and management of violence risk | Validation of static and dynamic risk instruments for violence; development of a Bayesian model for the assessment and management of offending behaviour | Development and validation of static and dynamic risk assessment instruments; development of a Bayesian model for the assessment and management of offending behaviour |
Section A Epidemiology of risk factors for violence in Great Britain
Chapter 2 Demography and typology of violence
Background
The public health impact of mental health on violence depends on the base rate of violence in the general population. This may ultimately influence whether targeted ‘high-risk’ or large-scale ‘population’ strategies are chosen for violence prevention. 3 In geographical locations with low violence rates, the proportion of violence attributed to mentally disordered people may appear high and efforts to contain their violence will achieve public health and political prominence. However, in locations with high base rates, more relevant risk factors may include weapon availability, substance misuse and gang violence, and being young, male, single and of low social class are the strongest risk factors for violence, irrespective of psychiatric morbidity. Demographic and social factors are prominent in most risk assessment instruments for violence and criminality. Factors such as younger age and criminological variables are contained in most tools and these, together with childhood factors indicating early onset of violence behaviour and substance misuse, appear to be the most predictive individual items for future violence. For some instruments, these are the only predictive items and clinical factors may have little predictive ability. 23 Nevertheless, there is a consensus that mental disorder is related to violence24–31 and that it increases the risk of violence over the lifespan. 32–36 Questions remain, however, over the size of the contribution from those with mental disorder to the overall level of violence within the general population and also over which disorders make the greatest contribution. The predictive ability of mental health variables to determine future violence will be dealt with in subsequent chapters. This current chapter will outline the association between mental health and violence at the population level in Great Britain, the size of the problem and whether or not the public health approach to violence is appropriate.
The public health problem of violence has generated less interest here than in the USA. 37,38 The UK has a relatively low rate of homicide. However, the high annual medical and social costs of injury from deliberate harm are highlighted by investigations in accident and emergency (A&E) departments in the UK. 39 Violence is a major public health problem that affects millions of people across the UK. The Crime Survey for England and Wales (CSEW)40 estimated that just over 2 million violent incidents were committed against adults in 2011/12. Over the same period, police recorded around 762,500 violence against the person offences in England and Wales. 41 A further 53,665 sexual offences were recorded. Just under half of violent incidents recorded by the police resulted in injury, as did half of those reported by adults to the CSEW. The difference between the number of violent incidents reported through the CSEW and the number of offences recorded by police shows that many incidents of violence are not recorded in the criminal justice system. Nevertheless, violence resulting in injury often requires treatment and there were 34,713 emergency hospital admissions for violence in England and Wales in 2010/11. 42 Rates were highest among young males and among those with increasing levels of socioeconomic deprivation. 43 For every hospital admission for violence it is estimated that a further 10 assault victims require treatment in A&E. A&E assault attendances peak at night over the weekend and are often related to alcohol. There were large increases in the levels of violence across England in the 1990s and early 2000s. However, more recent data suggest that the trend has reversed and that violence is decreasing. 44
Study 1: the demography of violence among adults in Great Britain
Objectives
The objectives of study 1 were to:
-
investigate the demographic characteristics of adults in the UK general population who report violence
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construct a typology of violence among men and women based on behavioural characteristics, including victims and the location of the violence.
Methods
Data from two national cross-sectional surveys and the commissioned second Men’s Modern Lifestyle Survey (MMLS) were used to identify high-risk behaviours, including violence, and demographic, psychiatric, lifestyle and service use correlates.
National surveys of psychiatric morbidity in the UK
People aged 16–74 years were sampled in the National Household Psychiatric Morbidity Survey (NHPMS) in 2000, details of which have been described previously. 45 This was a two-phase survey. 46 Computer-assisted interviews in person were carried out by Office for National Statistics interviewers. The small users Postcode Address File (www.poweredbypaf.com) was used as the sampling frame and the Kish grid method47 was applied to systematically select one person in each household. A total of 8886 adults completed the first-phase interview reported here, a response rate of 69.5%, and 8397 (94.5%) of these completed all sections of the questionnaire. Among non-respondents, 24% refused and 6.5% were non-contacts in the household. There was no information on psychiatric status of non-respondents to enable analysis of whether or not their omission resulted in biased estimates of the prevalence of violence. However, weighting procedures that were applied throughout the analyses took into account the proportions of non-respondents according to age, sex and region. This was to ensure a sample that was representative of the national population, compensating for sampling design and non-respondents in the standard error (SE) of the prevalence, and to control for the effects of selecting an individual per household.
The Adult Psychiatric Morbidity Survey (APMS) in 200748 sampled adults aged ≥ 16 years living in private households in England. In this survey, data were obtained from English adults only. The survey was commissioned by the NHS Health & Social Care Information Centre and was carried out by the National Centre for Social Research in collaboration with the University of Leicester. Field work was carried out between October 2006 and December 2007. A multistage stratified probability sampling design was adopted based on the small user Postcode Address File. One adult aged ≥ 16 years was selected from each household for interview using the Kish grid method. 47 Phase 1 data were collected by lay interviewers. A total of 7403 adults completed first-phase interviews, representing 57% of those eligible and originally approached. There is no information regarding the mental health status of non-respondents. However, data were again weighted to account for non-response. Sample weights were also assigned to take into account different probabilities in household selection. All models were corrected for area clusters based on postcodes. Of the 7403 participants, 7369 completed the violence self-report questions.
Men’s Modern Lifestyle Survey
Violence and high-risk behaviour at the population level disproportionately involves young men. However, young men are the least likely demographic group to voluntarily access health services, especially when confronted with emotional and social problems. 49,50 To investigate in more detail violence and use of services by young men, together with implications for risk management, we commissioned our own survey for the programme. This was carried out by ICM Research (www.icmunlimited.com).
The second MMLS was carried out in 2011. The MMLS was based on random location methodology, an advanced form of quota sampling shown to reduce the biases introduced when interviewers choose a location to sample from. Individual sampling units (census areas of 150 households each) were randomly selected within British regions, in proportion to their population. The basic survey derived a representative sample of young men (aged 18–34 years) from England, Scotland and Wales (n = 3247). In addition, there were four boost surveys. First, young black and minority ethnic men were selected from output areas with a minimum of 5% black and minority ethnic inhabitants (n = 1540) and young men from lower social grade D or E were selected from output areas with a minimum of 30 men aged 18–64 years in these social grades (n = 1002); The final boost surveys were based on output areas in two locations characterised by high levels of deprivation, the London borough of Hackney (n = 883) and Glasgow East (n = 789). The same sampling principles applied to each survey type.
Topics in the survey not included in the NHPMS or APMS included leisure activities, weight and exercise, use of pornography, enhanced information on antisocial and criminal behaviours and attitudes, including violence, harassment and stalking, gang membership and attitudes to accessing health-care services. Respondents completed the pencil and paper questionnaire in privacy and were paid £5 for participation.
Violence module
Participants in all surveys answered questions about the presence of violent behaviour. An affirmative answer to the question ‘Have you ever been in a physical fight, assaulted or deliberately hit anyone in the past 5 years?’ was followed by questions that qualified the violent events, including frequency, whether there were injuries or whether the act was committed while intoxicated. Additional questions assessed the identity of victims and the locations of the incidents.
Measurement of psychiatric morbidity
The Psychosis Screening Questionnaire (PSQ)51 was used to screen participants for psychosis, with a positive screening being one in which three or more criteria were met. Questions from the Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II) screening questionnaire52,53 identified antisocial personality disorder (ASPD) and borderline personality disorder (BPD) in all surveys.
In the MMLS, the Hospital Anxiety and Depression Scale (HADS)54 was used to define anxiety and depression, based on a score of ≥ 11 in the past week. In the NHPMS and APMS, the Clinical Interview Schedule Revised Version (CIS-R)55 was used to obtain the prevalence of common mental disorders in the week preceding the interview. These were combined into two categories of anxiety disorder and depressive disorder.
The principal instrument used to assess alcohol misuse was the Alcohol Use Disorders Identification Test (AUDIT),56,57 which defines hazardous drinking (a score of ≥ 8), alcohol misuse (a score of ≥ 16) and alcohol dependence (a score of ≥ 20). A number of questions designed to measure drug use were included in the NHPMS and APMS. Positive responses to any of five questions measuring drug dependence for a series of different substances over the previous year were combined to produce categories of drug dependence according to drug type. 45 Scores of ≥ 6 on the Drug Use Disorders Identification Test (DUDIT)58 were used to identify drug misuse and scores of ≥ 20 were used to identify drug dependence in the MMLS.
Analysis of data
Outcome data, including self-reported violent behaviour towards others and violent and sexual victimisation, including domestic violence, were examined in relation to measures of demography, general health, service use, common mental disorders, personality disorder, psychosis screen, adult attention deficit hyperactivity disorder (ADHD) and substance use, among others. Weighted analyses were used to account for the sampling procedure. Risks were measured by odds ratios (ORs) in all cross-sectional data. Multivariate statistical models were used to handle covariates among multiple outcomes of lifestyle and behaviour. Random-effects models were employed when area variation was thought to have a substantial effect on the outcome of interest.
Results
The weighted prevalence of severity and type, victims and location of violence in the three UK surveys (NHPMS 2000, APMS 2007 and MMLS 2011) is summarised in Table 2.
| Violence outcomes | NHPMS 2000, n (%) | APMS 2007, n (%) | MMLS 2011, n (%) |
|---|---|---|---|
| All participants, Na | 8382 | 7393 | 5240 |
| Any violence | 982 (11.7) | 614 (8.3) | 1681 (32.1) |
| Violence when intoxicated | 422 (5.0) | 263 (3.6) | 765 (14.9) |
| Repeated violence (five or more incidents) | 237 (2.8) | 98 (1.3) | 255 (5.0) |
| Victim injured | 333 (4.0) | 172 (2.3) | 720 (13.8) |
| Perpetrator injured | 310 (3.7) | 204 (2.8) | 713 (13.7) |
| Police involved | 254 (3.0) | 177 (2.4) | 410 (7.9) |
| Minor violence | 408 (4.9) | 247 (3.3) | 374 (7.2) |
| Gang fights | NA | NA | 266 (5.3) |
| Victim of violence | |||
| Intimate partner | 137 (1.6) | 115 (1.6) | 201 (3.9) |
| Family member | 63 (0.8) | 91 (1.2) | 223 (4.3) |
| Friend | 180 (2.1) | 132 (1.8) | 437 (8.4) |
| Someone known | 316 (3.8) | 195 (2.6) | 483 (9.3) |
| Stranger | 484 (5.8) | 300 (4.1) | 737 (14.1) |
| Police | 53 (0.6) | NA | 131 (2.5) |
| Location of violent incident | |||
| Own home | 168 (2.0) | 123 (1.7) | 235 (4.5) |
| Someone else’s home | 76 (0.9) | 61 (0.8) | 281 (5.4) |
| Street/outdoors | 555 (6.6) | 354 (4.8) | 855 (16.4) |
| Bar/pub | 358 (4.3) | 183 (2.5) | 622 (11.9) |
| Workplace | 81 (1.0) | 21 (0.3) | 60 (1.2) |
| At sporting event | NA | NA | 360 (7.2) |
The MMLS survey, which was restricted to men aged 18–34 years, reported the highest prevalence of violence in all categories, including violence when intoxicated, victim and perpetrator injured and violence repetition (i.e. five or more incidents). However, Table 2 also shows that, when the NHPMS and APMS are compared, the prevalence of all levels of severity and types of violence, victims and violence in specific locations was lower in the 2007 survey than in the 2000 survey among both men and women (except for violence against intimate partners and family members).
In terms of victims, violence towards strangers was the most prevalent category in the NHPMS, AMPS and MMLS. The rate of intimate partner violence (IPV) was similar in both household surveys and was higher among young men.
In each survey the most common location of violent incidents was in the street or outdoors. This was followed by violence in a pub or bar and violence in the respondent’s own home.
Age
Age was subsequently included as an adjustment in all models of association with violence in our studies. The mean age of violent men was 31.0 [standard deviation (SD) 11.6] years in the NHPMS, 32.0 (SD 12.7) years in the AMPS and 24.6 (SD 5.0) years in the MMLS, compared with 47.4 (SD 14.9) years, 52.7 (SD 17.7) years and 25.7 (SD 5.1) years, respectively, for non-violent men. Violent men were significantly younger than non-violent men in each survey (p < 0.001). The mean age of violent women was 30.1 (SD 9.9) years in the NHPMS and 31.4 (SD 11.8) years in the AMPS whereas the mean age of non-violent women was 46.3 (SD 15.4) years and 52.2 (18.5) years respectively. Violent women were significantly younger than non-violent women in each survey (p < 0.001).
Figure 2 shows the prevalence of self-reported violence in the last 5 years by age group (10-year age bands) for the two household surveys. In both surveys the prevalence of violence decreased with increasing age. This linear decrease was significant for both surveys (p < 0.001).
FIGURE 2.
Prevalence of any violence in the past 5 years in the UK by age group and survey.
The MMLS has a more restricted age range (18–34 years), which was divided into four age groups. Among those aged 18–20 years the prevalence of violence was 39.4%, among those aged 22–25 years prevalence was 34.8%, among those aged 26–29 years it was 28.5% and among the oldest age group of 30–34 years it was 27.3%. The linear trend for the effect of age on the prevalence of violence was highly significant (p < 0.001). Age was inversely associated with violence in all three surveys. Adjusted associations between age and any violence (Table 3) indicate that, compared with the youngest age group, increasing age exerts a protective effect on violence.
| Demographic characteristics | NHPMS 2000 | APMS 2007 | ||
|---|---|---|---|---|
| OR (95% CI) | AORa (95% CI) | OR (95% CI) | AORa (95% CI) | |
| Sex | ||||
| Female | Reference | Reference | Reference | Reference |
| Male | 3.74 (3.12 to 4.49)*** | 4.23 (3.39 to 5.27)*** | 2.94 (2.36 to 3.66)*** | 2.87 (2.22 to 3.71)*** |
| Age group (years) | ||||
| 16–34 | Reference | Reference | Reference | Reference |
| 35–54 | 0.24 (0.20 to 0.29)*** | 0.32 (0.26 to 0.40)*** | 0.23 (0.18 to 0.29)*** | 0.29 (0.22 to 0.38)*** |
| 55–74 | 0.04 (0.03 to 0.06)*** | 0.06 (0.04 to 0.08)*** | 0.05 (0.03 to 0.08)*** | 0.06 (0.03 to 0.09)*** |
| ≥ 75 | No data | No data | 0.01 (0.00 to 0.05)*** | 0.02 (0.01 to 0.07)*** |
| Marital status | ||||
| Married/widowed/cohabiting | Reference | Reference | Reference | Reference |
| Single | 6.60 (5.46 to 7.97)*** | 2.27 (1.83 to 2.83)*** | 5.34 (4.24 to 6.72)*** | 1.78 (1.35 to 2.35)*** |
| Separated/divorced | 2.53 (1.96 to 3.26)*** | 2.66 (2.03 to 3.49)*** | 1.63 (1.16 to 2.28)** | 2.03 (1.38 to 3.00)*** |
| Social class | ||||
| I | Reference | Reference | Reference | Reference |
| II | 1.37 (0.78 to 2.41) | 2.11 (1.15 to 3.87)* | 1.38 (0.57 to 3.32) | 1.73 (0.66 to 4.51) |
| IIIM | 2.10 (1.19 to 3.70)* | 3.87 (2.10 to 7.13)*** | 2.26 (0.94 to 5.41) | 2.93 (1.12 to 7.66)* |
| IIINM | 3.34 (1.93 to 5.78)*** | 4.22 (2.31 to 7.70)*** | 4.10 (1.75 to 9.63)** | 4.57 (1.81 to 11.54)** |
| IV | 3.13 (1.81 to 5.43)*** | 5.05 (2.78 to 9.14)*** | 3.26 (1.36 to 7.84)** | 3.64 (1.39 to 9.52)** |
| V | 2.60 (1.37 to 4.94)** | 4.65 (2.35 to 9.21)*** | 4.21 (1.67 to 10.62)** | 6.15 (2.22 to 17.03)*** |
| Ethnicity | ||||
| White | Reference | Reference | Reference | Reference |
| Black | 1.38 (0.85 to 2.26) | 0.82 (0.44 to 1.50) | 1.13 (0.58 to 2.18) | 0.86 (0.43 to 1.69) |
| South Asian | 0.59 (0.29 to 1.20) | 0.33 (0.14 to 0.76)** | 0.81 (0.42 to 1.57) | 0.45 (0.21 to 0.97)* |
| Other | 1.30 (0.72 to 2.37) | 1.18 (0.61 to 2.30) | 1.05 (0.58 to 2.18) | 0.77 (0.38 to 1.57) |
| Employment | ||||
| Employed | Reference | Reference | Reference | Reference |
| Unemployed | 2.60 (1.84 to 3.66)*** | 1.20 (0.78 to 1.84) | 3.08 (1.90 to 5.00)*** | 0.99 (0.57 to 1.74) |
Sex
Just over half of the participants in the NHPM 2000 and APMS 2007 surveys were women. Table 4 provides the frequencies and proportions of all violent outcomes by sex. Among men, the most prevalent violent outcome in 2000 was violence when intoxicated, towards strangers and taking place in the streets or outdoors and in bars/pubs. The same pattern was observed in 2007, but with a lower prevalence.
| Violence outcomes | NHPMS 2000 | APMS 2007 | ||
|---|---|---|---|---|
| Men | Women | Men | Women | |
| Any violence | 749 (18.0) | 233 (5.5) | 441 (12.3) | 173 (4.6) |
| Violence when intoxicated | 361 (8.7) | 61 (1.4) | 194 (5.4) | 69 (1.8) |
| Repeated violence (five or more incidents) | 193 (4.6) | 44 (1.0) | 82 (2.3) | 16 (0.4) |
| Victim injured | 287 (6.9) | 46 (1.1) | 132 (3.7) | 40 (1.0) |
| Perpetrator injured | 245 (5.9) | 65 (1.5) | 149 (4.2) | 55 (1.4) |
| Police involved | 202 (4.8) | 52 (1.2) | 129 (3.6) | 48 (1.3) |
| Minor violence | 282 (6.8) | 126 (3.0) | 174 (4.8) | 73 (1.9) |
| Victim of violence | ||||
| Intimate partner | 47 (1.1) | 90 (2.1) | 39 (1.1) | 76 (2.0) |
| Family member | 31 (0.7) | 32 (0.8) | 56 (1.6) | 35 (0.9) |
| Friend | 144 (3.5) | 36 (0.9) | 92 (2.6) | 41 (1.1) |
| Someone known | 252 (6.1) | 64 (1.5) | 144 (4.0) | 51 (1.4) |
| Stranger | 435 (10.4) | 48 (1.1) | 249 (6.9) | 51 (1.3) |
| Police | 47 (1.1) | 6 (0.1) | 26 (0.7) | 9 (0.2) |
| Location of violent incident | ||||
| Own home | 65 (1.6) | 104 (2.5) | 53 (1.5) | 70 (1.8) |
| Someone else’s home | 54 (1.3) | 22 (0.5) | 40 (1.1) | 21 (0.6) |
| Street/outdoors | 455 (10.9) | 100 (2.4) | 262 (7.3) | 92 (2.4) |
| Bar/pub | 307 (7.4) | 51 (1.2) | 136 (3.8) | 47 (1.2) |
| Workplace | 71 (1.7) | 9 (0.2) | 15 (0.4) | 6 (0.2) |
| Other | 123 (3.0) | 22 (0.5) | 78 (2.2) | 17 (0.5) |
The highest prevalence among women was for minor violence, IPV and violence taking place in the home. The prevalence of self-reported IPV was higher among women than among men. However, men were overall three times more likely to have engaged in any violence than women in both the NHPMS and the APMS (see Table 4).
Marital status
Marital status was coded according to three combined categories: (1) married, widowed or cohabiting, (2) single and (3) separated or divorced. In all statistical models category (1) was the reference group against which other categories were contrasted to estimate risk for violence. The prevalence of violence in the NHPMS was 4.9% among those who were married/cohabiting/widowed, 25.4% among those who were single and 11.6% among those who were separated or divorced. The prevalence of violence in the AMPS was 4.6% among those who were married/cohabiting/widowed, 20.4% among those who were single and 7.2% among those who were separated or divorced. The prevalence of violence in the MMLS was 26.6% among those who were married/cohabiting/widowed, 35.0% among those who were single and 36.1% among those who were separated or divorced. Tables 2 and 4 show the unadjusted and adjusted findings of the effects of marital status on violence, respectively.
Being single or separated/divorced was associated with a higher prevalence of violence throughout. In the household surveys, the likelihood of violence was approximately twofold among single and separated/divorced respondents. The odds of violence increased by approximately 50% among single and divorced young men in the MMLS.
Ethnicity
Ethnic groups recorded in the surveys were reclassified as white, black (originating from Africa or the West Indies), South Asian and ‘other’. In the NHSPM the prevalence of violence was 11.7% among white participants, 15.5% among black participants, 7.3% among South Asian participants and 14.7% among participants from other ethnic groups. In the AMPS the prevalence of violence was 8.3% among white participants, 9.3% among black participants, 6.9% among South Asian participants and 8.7% among participants from other ethnic groups. In the MMLS the prevalence of violence was 36.9% among white participants, 31.1% among black participants, 19.2% among South Asian participants and 21.1% among participants from other ethnic groups. The adjusted demographic models indicate that the South Asian ethnicity group was protective for any violence compared with white respondents in both household surveys (see Table 3). All black and minority ethnic groups in the MMLS were less likely to report violence than white respondents (Table 5).
| Demographic characteristics | MMLS 2011 | |
|---|---|---|
| OR (95% CI) | AORa (95% CI) | |
| Age group (years) | ||
| 18–24 | Reference | Reference |
| 25–34 | 0.65 (0.58 to 0.73)*** | 0.71 (0.62 to 0.82)*** |
| Marital status | ||
| Married/cohabiting | Reference | Reference |
| Single | 1.48 (1.30 to 1.69)*** | 1.27 (1.09 to 1.47)** |
| Separated/divorced | 1.56 (1.15 to 2.12)** | 1.50 (1.09 to 2.07)* |
| Social class | ||
| I and II | Reference | Reference |
| IIIM/IIINM | 1.06 (0.86 to 1.31) | 0.97 (0.78 to 1.21) |
| IV and V | 1.30 (1.05 to 1.61)* | 1.19 (0.95 to 1.48) |
| Unemployed | 1.53 (1.25 to 1.87)*** | 1.21 (0.97 to 1.50) |
| Ethnicity | ||
| White | Reference | Reference |
| Black | 0.77 (0.65 to 0.91)** | 0.77 (0.65 to 0.92)** |
| South Asian | 0.41 (0.34 to 0.48)** | 0.41 (0.34 to 0.49)*** |
| Other | 0.46 (0.27 to 0.78)** | 0.49 (0.28 to 0.86)* |
Immigration
Among the young men in the MMLS, 740 (14.0%) reported being born outside the UK. Of these, 99 (13.4%) reported any violence in the past 5 years; this compared with 1346 (30.6%) of those who were born in the UK. Those who were not born in the UK were significantly less likely to report any violence in a univariate model [OR 0.46, 95% confidence interval (CI) 0.36 to 0.58; p < 0.001] and after adjusting for all other demographic characteristics (OR 0.54, 95% CI 0.42 to 0.69; p < 0.001).
Social class and unemployment
Social class was based on the UK Registrar General’s classification,59 which was chosen because it uses the most recent occupation of head of household: I – professional, II – managerial, IIINM – skilled non-manual, IIIM – skilled manual, IV – partly skilled and V – unskilled. This classification provides an indicator of various domains including income, education and level of responsibility at work. 60,61
Table 6 shows the results for each category of social class (with class I as the reference) by sex. Among men in the NHPMS, all other categories of lower social class increased the risk for violence compared with class I. Participants from social classes IIINM and below were four times as likely to report violence in the past 5 years and social class V was associated with a fivefold increase in violence. However, among women, only social classes IIIM and V were associated with an increase in reported violence.
| Social class | n (%) | OR (95% CI) | AORa (95% CI) |
|---|---|---|---|
| NHPMS 2000 | |||
| Men | |||
| I | 22 (5.3) | Reference | Reference |
| II | 126 (10.2) | 1.71 (1.04 to 2.81)* | 1.94 (1.16 to 3.24)* |
| IIINM | 136 (27.5) | 5.71 (3.46 to 9.43)*** | 4.62 (2.74 to 7.79)*** |
| IIIM | 214 (18.1) | 3.32 (2.05 to 5.38)*** | 4.02 (2.43 to 6.64)*** |
| IV | 127 (22.3) | 4.31 (2.61 to 7.12)*** | 4.40 (2.61 to 7.42)*** |
| V | 45 (27.7) | 5.76 (3.24 to 10.23)*** | 5.08 (2.75 to 9.36)*** |
| Women | |||
| I | 22 (5.3) | Reference | Reference |
| II | 40 (3.6) | 1.58 (0.41 to 6.06) | 3.06 (0.68 to 13.74) |
| IIINM | 71 (4.8) | 2.14 (0.57 to 8.06) | 3.74 (0.84 to 16.57) |
| IIIM | 22 (6.8) | 3.11 (0.79 to 12.27) | 5.79 (1.25 to 26.92)* |
| IV | 66 (9.0) | 4.22 (1.12 to 15.93)* | 7.22 (1.62 to 32.14)** |
| V | 11 (4.0) | 1.76 (0.42 to 7.37) | 4.65 (0.94 to 22.95) |
| All | |||
| I | 21 (5.3) | Reference | Reference |
| II | 166 (7.1) | 1.37 (0.86 to 2.17) | 2.11 (1.30 to 3.42)** |
| IIINM | 207 (10.4) | 2.10 (1.33 to 3.31)** | 3.87 (2.38 to 6.28)*** |
| IIIM | 236 (15.6) | 3.34 (2.12 to 5.26)*** | 4.22 (2.62 to 6.81)*** |
| IV | 192 (14.8) | 3.13 (1.98 to 4.96)*** | 5.05 (3.10 to 8.21)*** |
| V | 56 (12.6) | 2.60 (1.55 to 4.35)*** | 4.65 (2.66 to 8.12)*** |
| APMS 2007 | |||
| Men | |||
| I | 8 (3.2) | Reference | Reference |
| II | 66 (6.0) | 1.96 (0.93 to 4.12) | 2.03 (0.92 to 4.50) |
| IIINM | 61 (14.8) | 5.34 (2.51 to 11.36)*** | 3.96 (1.77 to 8.86)** |
| IIIM | 142 (14.3) | 5.13 (2.48 to 10.60)*** | 6.16 (2.84 to 13.37)*** |
| IV | 73 (15.5) | 5.65 (2.68 to 11.92)*** | 4.72 (2.12 to 10.48)*** |
| V | 32 (26.2) | 10.92 (4.86 to 24.54)*** | 10.77 (4.45 to 26.07)*** |
| Women | |||
| I | 4 (3.6) | Reference | Reference |
| II | 36 (3.1) | 0.84 (0.29 to 2.43) | 0.99 (0.34 to 2.90) |
| IIINM | 47 (4.3) | 1.19 (0.42 to 3.38) | 1.33 (0.46 to 3.87) |
| IIIM | 14 (4.9) | 1.39 (0.44 to 4.33) | 1.56 (0.49 to 5.03) |
| IV | 38 (5.9) | 1.68 (0.58 to 4.82) | 1.69 (0.57 to 4.97) |
| V | 6 (3.3) | 0.91 (0.25 to 3.31) | 1.45 (0.36 to 5.79) |
| All | |||
| I | 12 (3.3) | Reference | Reference |
| II | 102 (4.5) | 1.38 (0.75 to 2.53) | 1.73 (0.91 to 3.29) |
| IIINM | 108 (7.1) | 2.26 (1.23 to 4.15)** | 2.93 (1.54 to 5.60)** |
| IIIM | 156 (12.2) | 4.10 (2.25 to 7.47)*** | 4.57 (2.42 to 8.63)*** |
| IV | 111 (10.0) | 3.26 (1.77 to 5.99)*** | 3.64 (1.91 to 6.95)*** |
| V | 38 (12.5) | 4.21 (2.16 to 8.22)*** | 6.15 (2.98 to 12.73)*** |
Certain differences were observed in the APMS. Social class was not significantly associated with violence among women; however, among men in social class V, the risk associated with violence was increased 10-fold.
There was a statistically significant trend in prevalence of violence according to lower social class for men (p < 0.001) and women (p < 0.001). However, in the NHPMS, we observed an unexpected 17.3% increase in the prevalence of violence among men from social class II to social class IIINM. This increase was statistically significant (10.2% vs. 27.5% respectively; p < 0.001). This contrasted with a 1.2% non-significant increase in self-reported violence among women from social class II to social class IIINM (3.6% vs. 4.8% respectively; p = 0.14). Compared with other categories, those men in social classes II and IIINM were younger (82.9% were aged 16–34 years vs. 67.1%; p < 0.008), more were single (81.1% vs. 56.6%; p < 0.001) and more were living with their parents (34.0% vs. 11.8%, p < 0.001). In addition, fewer lived in rented accommodation (25.0% vs. 40.1%, p < 0.01) and as a couple (21.9% vs. 51.7%, p < 0.001). Violent men in social class IIINM were approximately three times more likely to be single and living with their parents than violent men in other social classes.
Discussion
Sex and age differences in violent behaviour identified in our study are in accordance with a previous meta-analysis. 62 Physical aggression is more common among men than women at all ages and this is consistent across cultures, appearing from early childhood onwards and showing a peak between 20 and 30 years of age. However, studies that have measured anger did not show sex differences. Higher levels of indirect aggression among females, such as expressions of anger, are limited to later childhood and adolescence, but tend to vary according to the methods of measurement and were not included in this study.
Sexual selection theory hypothesises that the origin of greater male physical aggression in human evolutionary history is a consequence of unequal parental investment, leading to greater male than female reproductive competition and, therefore, overt aggression. 63 This is thought to be the psychological accompaniment of physical sex differences such as size, strength and longevity. 62 Evolutionary analyses have identified different degrees of risk that an individual is prepared to take during a conflict as a crucial difference between the sexes. Greater variation in male and female reproductive success among mammals leads to more intense male competition. Selection favours high-risk strategies (even when mortality rates are high) if the reward of victory is high and the consequence of losing is little or no chance of reproducing. 64 This theoretical approach suggests that sex differences and physical aggression will be largest when reproductive competition is highest, for example during young adulthood, and can include higher risk and escalated forms of aggression, such as those involving death or severe injury. Our findings are in agreement with this theory in demonstrating that men in early adulthood were more likely to engage in severe violence against strangers and people known to them. Weissfeld65 has argued that boys compete in this way to form dominance orders or hierarchies. This has been compared with the behaviour of other primates and is thought to be important for providing access to resources, including reproductive success in social animals. Hierarchies are based on dominance and the use of aggression, which is stable over time, together with high dominance. This appears to rank with certain other attributes, including personality.
All levels of severity of violence and all victim types were more prevalent among men than women, except violence against intimate partners and family. Women were approximately twice as likely to report an intimate partner as a victim than men. However, national surveys have shown that women who are married or cohabiting are more likely to report fear of bodily injury, actual injuries and the use of medical, mental health and criminal justice system services as a result of IPV than men. 66 Similarly, substantially more women report that they have experienced sexual violence from an intimate partner,67 which was not included in this study. Nevertheless, Dutton and Nicholls68 have argued that the sex disparity in injuries from domestic violence is less than originally portrayed by feminist theory. In a review of studies,69 high levels of unilateral intimate violence by women towards both men and women have been observed. Furthermore, men report their own victimisation less often than women and do not view female violence against them as a crime. As a result, male victimisation by female partners is under-reported in crime victim surveys.
Married men are less likely to commit crimes, including violence. However, there are questions of selection and confounding when studying this relationship. Samson et al. 70 carried out a study of high-risk boys followed up prospectively from adolescence to age 32 years. They found that being married was associated with an average reduction of approximately 35% in the odds of committing a crime compared with not being married. Previous research has also indicated that marriage is a key turning point in desistance from crime. 71 The establishment of a good relationship is thought to facilitate this. Correspondingly, Bersani et al. 72 found that marriage reduced offending, including violent offending, for both men and women in the Netherlands. Laub et al. 73 argue that a close relationship acts protectively on crime and violence by the formation of social bonds and an investment process in the relationship. It is the quality of the marital bond that affects this. However, the influence is gradual and is cumulative over time. Our finding that divorce and separation were strongly associated with violence in this study corresponds to this. However, the processes leading to the breakdown of a relationship are likely to be complex and could reflect an individual’s tendency to violence or even be the result of violence towards the intimate partner.
The association between violent crime and single marital status could be explained by social factors in the lives of young people and was independent of age. The move to social independence among young men is important. Although many young men now remain in the parental home, this was not found to have a protective effect among young men in Great Britain. 74 Furthermore, violence when intoxicated observed among young men (see Chapter 6, Study 2) and fighting with strangers can be construed as one example among a series of hedonistic and negative social behaviours (including hazardous drinking, drug misuse, sexual risk taking and non-violent antisocial behaviour) exhibited by single young men without the responsibilities of providing their own accommodation or supporting dependent children or the ameliorating effects on their behaviour of living with a female partner. It has been questioned whether or not this lifestyle has become more prevalent among some men within the context of increasing prolongation of early adulthood and when it now takes longer to obtain a full-time job that pays sufficiently to support a family. 75 US research has indicated that many young people in their early 20s have not become fully adult according to their own subjective assessment and do not perceive themselves as either ready or able to perform these roles. A comparison of census data in the USA from the years 1960 and 200076 demonstrated that fewer men aged 40 years in 2000 than in 1960 had completed all the major transitions of leaving home, finishing school, becoming financially independent, getting married and having a child. Furthermore, young people remaining at home now receive more substantial financial aid from their parents than previous generations. 75 Not having to provide accommodation or support dependents means a relatively higher disposable income and more leisure time, possibly associated with higher-risk activities, including violence.
We did not find differences in self-reported violence in our surveys between black and minority ethnic subgroups and white study participants. Being of South Asian origin appeared to convey a protective effect. This was before adjusting for other demographic factors, most importantly social class. This finding is in marked contrast to the number of stop and search encounters and convictions for violent crime in black and minority ethnic populations and proportions of people imprisoned from a black and minority ethnic origin in the UK. Similarly, the lack of an association between immigration and violence in this study is consistent with the lack of an association between immigration and crime in the UK. 77 The continuous reduction in the number of overall property crimes in England and Wales since 2002 has occurred in the face of an increasing foreign-born population, but there is no evidence to suggest that rising migration causes a decline in crime rates. The foreign-born proportion of the population is also unrelated to violent crime according to most recent research findings. 78
It has long been established that the strength of the relationship between social class and violence varies significantly, but depends primarily on the measure of social class. 79 Our finding that in 2000 there was a higher than expected prevalence of violence among men from social classes II and IIINM is most likely explained by the characteristics of the men in these two social groups. For example, an unexpectedly large number of men still living in the parental home, not having children or not being involved in a relationship contributed substantially to the association between social class IIINM and violent behaviour.
It is thought that unemployment has a key part to play in violence and that lack of routine activity and the economic effects of being unemployed increase the risk of crime. However, research has indicated that violent crime, as opposed to burglary and theft, is pro-cyclical: higher in good times when unemployment is lower. It has been argued that alcohol consumption, which is higher in good times and more strongly related to violence, is a key determinant. 80 Nevertheless, the association between violence and unemployment is highly complex and other associated factors are highly relevant. Lack of finances is important because labour markets are important sources of status and the focus of struggles over norms of fairness and a validated identity. Unemployment can be relevant to violence when it intersects with collective identities: masculinity, race or ethnicity or religion. When there are no structured institutional mechanisms for unemployed people to express complaints and press for improvement, the chances are increased that there may be one or other type of violence. This may also apply to IPV. At the international level, the contribution of the labour market structure and opportunities and relations to violent conflicts cannot be understood in isolation from the broader structural and policy features of a society. 81
Study 2: a typology of violent persons in the population
Aims and objective
The aims of study 2 were to:
-
Identify groups, or subtypes, of people in the population of Great Britain according to their patterns of self-reported violent behaviour. We included measures of severity and frequency, their victims and the location of their violence to determine subtypes.
-
Validate these subtypes according to their differing demographic characteristics.
The overall objective was to create a typology for investigating the associations with psychiatric morbidity in subsequent studies in this section of the report.
Methods
Participants
We used a combined sample of men and women drawn from the first phase of the NHPMS 2000 and the APMS 2007 (see Study 1). Design and sampling procedures have previously been described. 45,82 As each of the surveys employed the same measures of demography and violence outcomes, we conducted joint analyses of individual-level data. All analyses on violence typologies were carried out separately by sex.
Measures
We used the self-reported measures of violence described in study 1. Social class was based on the UK Registrar General’s classification,59 which uses the most recent occupation of the head of the household. Sociodemographic covariates included age in 20-year bands, marital status, ethnicity and employment.
Latent class analysis
We used latent class analysis (LCA) to explore whether or not individuals could be classified into a set of latent variables based on their endorsement of the violence indicators. Membership of these subgroups, often called latent classes, is defined by the specific set of responses to a series of observed characteristics. This approach allowed us to describe the relationships of the variables as they combined into classes that defined groups of people within a sample or population.
Latent class analysis was used to empirically define participant groups based on their violent behaviours profile and explore the existence of typologies of violence. Decisions regarding the most appropriate model were led by statistical indicators and clinical considerations. The default estimator was the robust maximum likelihood (MLR). However, MLR may lead to the presence of a problem called local maxima. To fully avoid this, all LCA models were estimated with different random starting values: we used 2000 random starts at the initial stage and 200 optimisations at the final stage. Models were inspected to ensure that the log-likelihood value for each model was successfully replicated several times (an indication of low probability of local maxima). We gave priority to this rule in selecting our final latent class model.
After selecting the classes that fitted best by sex, we described them in terms of the aforementioned demographic characteristics.
All analyses were performed using Mplus software (Muthéu and Muthéu, Los Angeles, CA, USA) for Windows OS version 7.11.
Results
Typology of violence among men
To identify the constructs in each class (classification) included in Table 7, we established indicators with probabilities of < 0.29 (low probability), from 0.30 to 0.59 (moderate probability) and > 0.60 (high probability). We classified men into the following classes: class 1 – ‘no violence’; class 2 – ‘minor violence’, characterised by fights with strangers, persons known and friends, in the street or in bars, with few incidents or only one incident and in which no-one is injured; class 3 – ‘violence towards known persons/family’, characterised by more serious violence resulting in injuries to the victim and perpetrator, involving a range of different victims but mainly persons known, friends, family members and intimate partners and in a range of locations, mainly in the street or in bars but also in their own or another’s home; class 4 – ‘fighting with strangers’, characterised by fights almost exclusively with strangers taking place in the street or in bars, often when intoxicated and leading to injury to the victim or perpetrator (one in five men in class 4 had been involved in multiple violent incidents with strangers); and class 5 – ‘serious repetitive violence’, characterised by multiple incidents of violence, usually when intoxicated, resulting in injuries to multiple victims and in multiple locations and including family and intimate partners (see Table 7).
| Violence indicators | Class 1 (N = 6583; 84.8%), n (%) | Class 2 (N = 453; 5.8%), n (%) | Class 3 (N = 296; 3.8%), n (%) | Class 4 (N = 308; 4.0%), n (%) | Class 5 (N = 121; 1.6%), n (%) |
|---|---|---|---|---|---|
| Repeated violence (five or more incidents) | 1 (0.0) | 66 (14.6) | 43 (14.5) | 63 (20.6) | 102 (84.5) |
| Violent when intoxicated | 0 (0.0) | 155 (34.2) | 142 (47.8) | 158 (51.3) | 100 (83.5) |
| Victim injured | 4 (0.1) | 0 (0.0) | 138 (46.4) | 157 (51.1) | 121 (100.0) |
| Perpetrator injured | 0 (0.0) | 0 (0.0) | 172 (58.0) | 137 (44.6) | 85 (70.4) |
| Minor violence | 0 (0.0) | 453 (100.0) | 0 (0.0) | 1 (0.3) | 2 (1.7) |
| IPV | 0 (0.0) | 25 (5.6) | 37 (12.6) | 0 (0.0) | 23 (19.2) |
| Towards a family member | 0 (0.0) | 31 (6.8) | 39 (13.3) | 3 (0.9) | 14 (11.7) |
| Towards a friend | 0 (0.0) | 86 (19.0) | 84 (28.5) | 16 (5.3) | 49 (40.9) |
| Towards someone known | 0 (0.0) | 134 (29.5) | 177 (59.8) | 2 (0.7) | 83 (68.7) |
| Towards a stranger | 0 (0.0) | 237 (52.3) | 31 (10.6) | 308 (100.0) | 108 (89.2) |
| In the home | 0 (0.0) | 34 (7.4) | 58 (19.5) | 0 (0.0) | 27 (22.2) |
| In someone else’s home | 0 (0.0) | 26 (5.7) | 33 (11.3) | 13 (4.1) | 22 (18.3) |
| In the street/outdoors | 5 (0.1) | 206 (45.5) | 182 (61.4) | 212 (68.8) | 111 (92.2) |
| In a bar/pub | 0 (0.0) | 144 (31.9) | 76 (25.7) | 112 (36.5) | 110 (91.2) |
| In the workplace | 2 (0.0) | 30 (6.7) | 15 (5.0) | 15 (4.8) | 25 (20.7) |
Several models of latent subgroups defined by violence indicators were estimated for men and women. Complex sampling and weights were considered in the development of the latent class models. Model fit and information criteria for LCA model selection are included in Table 8 for men and Table 9 for women. Model fit indices favoured the five-class model in men. A three-class model provided the best fit to the data in women.
| Model | Log-likelihood | Replicated log-likelihood | AIC | BIC | aBIC | VLMR-LRT p-value | Entropy |
|---|---|---|---|---|---|---|---|
| Class 1 | 17590.6 | Yes | 35211.3 | 35313.9 | 35266.3 | NA | NA |
| Class 2 | 11163.4 | Yes | 22388.8 | 22601.0 | 22502.5 | < 0.0001 | 0.99 |
| Class 3 | 10638.0 | Yes | 21370.1 | 21691.8 | 21542.4 | < 0.0001 | 0.99 |
| Class 4 | 10484.1 | Yes | 21094.2 | 21525.4 | 21325.2 | < 0.0001 | 0.99 |
| Class 5 | 10392.1 | Yes | 20816.1 | 21356.8 | 21105.8 | 0.0002 | 0.98 |
| Model | Log-likelihood | Replicated log-likelihood | AIC | BIC | aBIC | VLMR-LRT p-value | Entropy |
|---|---|---|---|---|---|---|---|
| Class 1 | 9320.7 | Yes | 18671.3 | 18777.6 | 18729.9 | NA | NA |
| Class 2 | 5360.1 | Yes | 10782.2 | 11001.9 | 10903.4 | < 0.0001 | 0.99 |
| Class 3 | 5145.5 | Yes | 10385.1 | 10718.1 | 10568.8 | < 0.0001 | 0.99 |
Table 10 shows that members of class 5 (‘serious repetitive violence’) were younger than members of the other classes, with no men in the older age group (55–74 years), no black men and predominantly white and single men. Most were employed in occupations from social classes IIIM and IIINM. Class 4 showed similar demographic characteristics, with significantly fewer Asian men and fewer separated or divorced men. Class 3 had the largest proportion of separated or divorced men and one-quarter were economically inactive. Class 2 showed few differences from class 1 (non-violent men), except that more were younger and single.
| Demographic characteristics | Violence typologies (latent classes) | ||||
|---|---|---|---|---|---|
| Class 1 (%) (reference) | Class 2 (%) | Class 3 (%) | Class 4 (%) | Class 5 (%) | |
| Age group (years) | |||||
| 16–34 (reference) | 27.3 | 75.3 | 70.9 | 69.2 | 82.7 |
| 35–54 | 40.4 | 21.9a | 24.0a | 26.6a | 17.3a |
| 55–74 | 32.3 | 2.8a | 5.1a | 4.2a | 0.0a |
| Ethnicity | |||||
| White (reference) | 90.8 | 88.6 | 93.4 | 95.7 | 96.7 |
| Black | 2.7 | 5.1 | 2.5 | 1.6 | 0.0a |
| South Asian | 4.0 | 3.1 | 2.6 | 1.0a | 2.3 |
| Other | 2.5 | 3.3 | 1.5 | 1.7 | 1.1 |
| Social class | |||||
| I and II (reference) | 42.8 | 23.4 | 17.9 | 23.6 | 10.6 |
| IIIM and IIINM | 40.5 | 49.0b | 54.7b | 48.9b | 70.3b |
| IV and V | 16.7 | 27.6b | 27.4b | 27.6b | 19.1b |
| Marital status | |||||
| Married or cohabiting (reference) | 67.9 | 27.0 | 36.3 | 35.8 | 26.7 |
| Single | 24.3 | 66.9b | 54.6 | 57.2b | 67.4b |
| Separated/divorced | 7.8 | 6.1b | 9.0b | 7.0b | 6.0 |
| Employment | |||||
| Employed (reference) | 70.2 | 74.8 | 66.2 | 81.8 | 73.5 |
| Unemployed | 3.1 | 8.5 | 8.5 | 6.5 | 4.6 |
| Inactive economically | 26.7 | 16.7 | 25.3b | 11.7 | 22.0 |
We carried out a further investigation to see to what extent IPV had determined the classes. We found that only 24 (3.1%) men reported that they were uniquely violent towards their intimate partner, indicating that IPV had little effect in determining the classes among men.
Typology of violence among women
As with men, we established that indicators with a probability < 0.29 as low, from 0.30 to 0.59 as moderate probability and above 0.60 as high probability among women. Table 11 shows that class 1 was characterised by ‘no violence’ and had a prevalence of 94.9%. Class 2 (‘general violence’) was characterised by a range of different victims, mainly persons known and strangers, but also family members, intimate partners and friends, with violence occurring usually in the street or in a bar, but also in the perpetrator’s own or someone else’s home. This class resembled class 3 in men. Class 3 (‘intimate family violence’) was characterised by violence occurring exclusively in the home and involving intimate partners and family members. It usually involved minor violence and if a participant was injured it was usually the female perpetrator. Class 2 included the highest proportion of women of young age (16–34 years) (83%) followed by class 3 (58.3%). Ethnic composition was similar across the classes. Class 2 had significantly higher proportions of women in lower social classes. Women in classes 2 and 3 were mainly single, with more women in class 3 separated or divorced. There was no association with employment status (Table 12).
| Violence indicators | Class 1 (N = 7608; 94.9%), n (%) | Class 2 (N = 282; 3.5%), n (%) | Class 3 (N = 124; 1.5%), n (%) |
|---|---|---|---|
| Repeated violence (five or more incidents) | 0 (0.0) | 35 (12.4) | 25 (20.0) |
| Violent when intoxicated | 0 (0.0) | 108 (38.2) | 22 (18.2) |
| Victim injured | 0 (0.0) | 76 (27.1) | 9 (7.5) |
| Perpetrator injured | 0 (0.0) | 88 (31.2) | 32 (25.7) |
| Minor violence | 0 (0.0) | 119 (42.4) | 79 (64.3) |
| IPV | 0 (0.0) | 35 (12.6) | 98 (79.3) |
| Towards a family member | 0 (0.0) | 68 (24.2) | 31 (25.1) |
| Towards a friend | 0 (0.0) | 76 (27.1) | 0 (0.0) |
| Towards someone known | 0 (0.0) | 115 (40.7) | 0 (0.0) |
| Towards a stranger | 0 (0.0) | 99 (35.2) | 0 (0.0) |
| In the home | 0 (0.0) | 50 (17.6) | 124 (100.0) |
| In someone else’s home | 0 (0.0) | 36 (12.9) | 7 (5.9) |
| In the street/outdoors | 0 (0.0) | 176 (62.4) | 17 (13.6) |
| In a bar/pub | 0 (0.0) | 93 (33.1) | 5 (3.9) |
| In the workplace | 0 (0.0) | 14 (5.0) | 1 (0.6) |
| Demographic characteristics | Violence typologies (latent classes) | ||
|---|---|---|---|
| Class 1 (%) (reference) | Class 2 (%) | Class 3 (%) | |
| Age group (years) | |||
| 16–34 (reference) | 30.2 | 83.0 | 58.3 |
| 35–54 | 37.8 | 15.9a | 36.1a |
| 55–74 | 32.0 | 1.1a | 5.6a |
| Ethnicity | |||
| White (reference) | 92.8 | 90.4 | 93.1 |
| Black | 2.4 | 3.0 | 4.5 |
| South Asian | 2.8 | 2.0 | 1.0 |
| Other | 2.1 | 4.7 | 1.4 |
| Social class | |||
| I and II (reference) | 33.6 | 18.9 | 31.5 |
| IIIM and IIINM | 42.5 | 44.8b | 39.6 |
| IV and V | 23.9 | 36.4b | 29.0 |
| Marital status | |||
| Married or cohabiting (reference) | 68.4 | 20.3 | 35.7 |
| Single | 21.1 | 68.7b | 42.1b |
| Separated/divorced | 10.5 | 11.0b | 22.1b |
| Employment | |||
| Employed (reference) | 57.4 | 62.5 | 70.6 |
| Unemployed | 2.1 | 5.2 | 1.5 |
| Inactive economically | 40.6 | 32.3 | 27.8 |
Discussion
We identified five classes of violent men, which we further validate against categories of psychiatric morbidity and other psychopathology in Chapters 3–9 of this section of the report. Class 2 included men who had become involved in minor violence, usually on a ‘one-off’ basis. Apart from being young and single (the most common risk factors for becoming involved in violence), there was little to differentiate them from men who were not violent in the population.
Men in class 3 (‘violence towards known persons/family’) engaged in more serious violence than men in class 2: they and their victims became injured. Violence involved victims known to the perpetrators, including friends, family and intimate partners, and occurred in a range of different locations. The higher prevalence of divorced and separated men in this group and the finding that one-quarter were economically inactive suggests that they were poor at sustaining relationships and friendships and that many were dependent on state benefits.
Men in class 4 (‘fighting with strangers’) were involved in more serious violence leading to injuries, almost exclusively with people previously unknown to them, sometimes in multiple violent encounters. The narrower range of characteristics suggests a lifestyle in which potential violent altercations with other young men are likely to occur, such as in bars or encounters in the street, or at sporting activities, and/or that these men actively seek such altercations, finding violence exciting and a personal challenge.
Class 5 was the most distinct subgroup, characterised by serious violence to the widest range of victims. Men in this class had similar altercations with strangers as in class 4, but their violence also extended to their friends, family members and intimate partners, in multiple different locations. Similar observations have previously been made for those in the community with a diagnosis of ASPD. 83 Because the study was cross-sectional, it could not be investigated whether a subgroup will eventually mature or ‘burn out’. This might explain the concentration of class 5 men in the youngest age group. However, although class 5 appeared to be the most serious public health risk to others as a result of violence, and their repetitive violence suggested the probability of greater psychological dysfunction and psychiatric morbidity (which will be investigated in subsequent chapters), other characteristics, such as a concentration of these men in social classes IIINM and IIIM and the absence of black men from this class, were of considerable interest.
Intimate partner violence did not determine a unique group among men as observed with class 3 women. Very few men in the study reported that their violence was uniquely directed against partners. For men, IPV was, therefore, an indication of a general violent disposition, extending to strangers and others outside of the home. For women, the situation was different. A considerably larger proportion reported that their violence was exclusively directed against partners or family members. This violence was largely minor, with no-one being injured. If someone was injured in the violent altercation, it was most likely the woman herself, most probably by her male partner. It was noteworthy that the social class of class 3 women was not significantly different from that of non-violent women.
Class 2 women were younger and of a lower social class and engaged in violence towards a range of different victims, but most commonly persons known to them. There was a similarity to class 3 and to a lesser extent class 5 among men. This group, exhibiting violence with similarities to men’s violence, is therefore of considerable interest. It is hypothesised that, to become violent and antisocial, women must surmount a higher threshold of risk than men and are therefore more severely afflicted (‘threshold/paradox’ hypothesis). 84 The female threshold is presumed to be raised by the sex role socialisation of women against aggression at the level of culture. The risk over this threshold is presumed to come from psychobiological or developmental factors at the level of the individual. If more severe aetiology is found for women, then the inference can be made that a higher threshold for women exists. 85,86
Chapter 3 Psychiatric morbidity and violence
Background
It has long been assumed that there is a link between major mental illness and violence. This belief is prevalent across cultures: community surveys show that the general population often associates mental disorder with violence. More recently, however, a lack of association has been reported in certain studies of mentally disordered offenders, particularly for schizophrenia. 87,88 The same conclusion was reached in a meta-analysis of variables associated with recidivism among mentally disordered offenders,89 which found that the average association between psychosis and violence was small and negative across 11 studies that reported on psychosis and that demographic factors and previous violence were the strongest risk factors for future violence.
The uncertainty extends to other diagnoses, including anxiety and depression. Coid and Ullrich90 found an association between increased levels of violence and anxiety when comorbid with ASPD. However, it remains unclear if anxiety is important as a risk factor at the general population level. Depressive disorder is not thought of as a common risk factor and depression may act as a factor that reduces the risk of violence for some individuals. Douglas et al. 91 have suggested that severe mental disorder might be causally associated with violence. In this case, symptoms of mental disorder might provide a clear motivation for violence or interfere with the ability to manage interpersonal conflict. On the other hand, severe mental disorder might be a consequence of violence. This stress of perpetrating violence could trigger the onset of mental disorder in people who are so predisposed. Finally, severe disorder may be a simple correlate of violence. The association between the two may be statistical rather than causal, the result of links with some third variable such as stressful life events, lack of social support, personality traits, substance use or victimisation. Douglas et al. 91 suggested that, if this explanation was true, it would be expected that there would be no clear time-related or statistical association between severe mental disorder and psychosis, at least after controlling for potential confounding factors.
Study 1: anxiety disorder
Objectives
The objectives of this study were to:
-
Investigate the associations between anxiety disorder and self-reported violence towards others. We investigated associations between five individual categories of anxiety disorder in the International Statistical Classification of Diseases and Related Health Problems, Tenth Edition (ICD-10),92 and a combined category.
-
Investigate the associations between anxiety disorder and the classes of violence identified in the population in Chapter 2 (see Study 2).
Methods
Sample
For the purposes of this analysis we combined two data sets, the APMS 2007 and the NHPMS 2000, to provide a total of 15,734 subjects. 45,49
Definition and assessment
Anxiety disorders in the combined category included generalised anxiety disorder, panic disorder, mixed anxiety disorder and depression, obsessive–compulsive disorder and phobias. These were assessed using the CIS-R55 and were coded according to ICD-10 criteria. The CIS-R assesses 14 neurotic syndromes by asking respondents two screening questions in each of the sections, namely somatic symptoms, fatigue, concentration and forgetfulness, sleep problems, irritability, worry about physical health, depression, depressive ideas, worry, anxiety, phobias, panic, compulsions and obsessions. For positive responses, further questions were asked to ascertain the duration, frequency and severity of the neurotic symptomatology. These were rated from 0 to 4, except for depression, which is rated from 0 to 5. Mixed anxiety and depressive disorder was diagnosed when a threshold score of 12 on the CIS-R was reached without the diagnostic criteria for depression or any anxiety disorder being met.
The violent outcomes module questions have been previously described in Chapter 2 (see Violence module).
Associations between anxiety disorders and any violence in the past 5 years were examined. Risks were measured using ORs. Weighted analyses were used to account for the sampling procedure in the surveys, as described in Chapter 2.
Results
Table 13 shows that 1596 (10.1%) individuals in the combined survey samples reported any violence in the past 5 years. Violence was more common among men than women, among younger adults and among those from lower socioeconomic groups.
| Covariate | Any violence in the past 5 years (n = 1596; 10.1%) | Anxiety classification (n = 2437; 15.3%) | ||
|---|---|---|---|---|
| n (%) reported | AOR (95% CI) | n (%) reported | AOR (95% CI) | |
| Sex | ||||
| Female | 406 (5.1) | Reference | 1479 (18.3) | Reference |
| Male | 1190 (15.4) | 3.16 (2.68 to 3.72)*** | 958 (12.2) | 0.53 (0.47 to 0.59)*** |
| Age group (years) | ||||
| 16–34 | 1175 (22.4) | Reference | 849 (16.0) | Reference |
| 35–54 | 371 (6.3) | 0.34 (0.28 to 0.40)*** | 1048 (17.6) | 1.21 (1.06 to 1.39)** |
| 55–74 | 51 (1.1) | 0.07 (0.05 to 0.09)*** | 540 (11.5) | 0.79 (0.68 to 0.92)** |
| Marital status | ||||
| Married | 480 (4.7) | Reference | 1411 (13.7) | Reference |
| Single | 973 (23.4) | 1.74 (1.46 to 2.08)*** | 679 (16.1) | 0.96 (0.83 to 1.11) |
| Divorced/separated | 144 (9.9) | 2.09 (1.67 to 2.63)*** | 346 (23.5) | 1.58 (1.39 to 1.81)*** |
| Social class | ||||
| I and II | 302 (5.6) | Reference | 725 (13.3) | Reference |
| IIIM and IIINM | 708 (11.3) | 2.11(1.75 to 2.53)*** | 990 (15.5) | 1.15 (1.03 to 1.29)* |
| IV and V | 398 (12.6) | 2.44 (1.97 to 3.04)*** | 578 (18.0) | 1.27 (1.11 to 1.45)** |
| Ethnicity | ||||
| White | 1463 (10.2) | Reference | 2177 (14.9) | Reference |
| Black | 49 (12.1) | 0.95 (0.60 to 1.50) | 73 (17.6) | 1.15 (0.84 to 1.56) |
| Indian subcontinent | 36 (7.0) | 0.47 (0.26 to 0.84)* | 85 (16.6) | 1.39 (0.98 to 1.97) |
| Other | 40 (11.2) | 0.92 (0.55 to 1.54) | 75 (20.4) | 1.27 (0.89 to 1.81) |
| ASPD | 246 (46.2) | 3.09 (2.29 to 4.17)*** | 195 (35.9) | 2.36 (1.83 to 3.05)*** |
| Drug dependency | 276 (48.4) | 2.47 (1.86 to 3.29)*** | 193 (33.8) | 2.04 (1.56 to 2.67)*** |
| Alcohol dependency | 371 (35.3) | 2.36 (1.89 to 2.95)*** | 313 (29.4) | 2.37 (1.96 to 2.86)*** |
| Psychosis | 7 (14.0) | 0.72 (0.20 to 2.63) | 42 (77.3) | 14.59 (6.37 to 33.43)*** |
We found that 2437 (15.3%) individuals met ICD-10 criteria for anxiety disorders. Anxiety disorders were significantly more common among females, those who were aged > 55 years and those who were divorced.
Table 14 shows that, following adjustments for demographics (age, sex, marital status, ethnicity and social class), any violence was significantly associated with anxiety disorder, phobia, obsessional and compulsive disorder, mixed anxiety and depression, but not panic disorder.
| Exposure | n (%) violent | AORa (95% CI) | AORb (95% CI) |
|---|---|---|---|
| Anxiety (any) | 394 (24.7) | 2.44 (2.06 to 2.89)*** | 1.91 (1.59 to 2.29)*** |
| Panic disorder | 22 (1.4) | 1.38 (0.80 to 2.38) | 0.99 (0.55 to 1.79) |
| Any phobia | 66 (4.1) | 3.03 (2.02 to 4.54)*** | 2.08 (1.32 to 3.27)** |
| OCD | 42 (2.6) | 2.98 (1.88 to 4.72)*** | 2.13 (1.29 to 3.54)** |
| GAD | 103 (6.5) | 2.18 (1.63 to 2.91)*** | 1.77 (1.29 to 2.42)*** |
| MADD | 91 (5.7) | 2.11 (1.51 to 2.94)*** | 1.90 (1.31 to 2.75)** |
| Depression | 79 (4.9) | 2.33 (1.67 to 3.24)*** | 1.42 (0.96 to 2.09) |
When further adjusted for alcohol, drug dependency and psychiatric comorbidity (ASPD, psychosis and depression), any violence remained significantly associated with a combined category of any anxiety disorder, phobic disorder, obsessive–compulsive disorder, generalised anxiety disorder and mixed anxiety and depression, but not panic disorder or depression.
Table 15 shows that, when different subtypes of violence were examined, anxiety disorder (combining the previously described five categories of anxiety disorders and excluding depression) was significantly associated with violence while intoxicated, minor violence, repetitive violence and violence that resulted in a victim sustaining injury after adjusting for demographic factors. However, following adjustment for demographic factors, anxiety disorder was not significantly associated with violence in the workplace.
| Outcomes | n (%) violent | AORa (95% CI) | AORb (95% CI) |
|---|---|---|---|
| Any violence | 394 (16.5) | 2.00 (1.73 to 2.32)*** | 1.91 (1.59 to 2.29)*** |
| Violence while intoxicated | 192 (8.0) | 2.28 (1.87 to 2.78)*** | 1.76 (1.36 to 2.28)*** |
| Severity of violence | |||
| Minor violence | 130 (5.4) | 1.40 (1.12 to 1.76)** | 1.57 (1.22 to 2.03)** |
| Five or more violent incidents | 95 (3.9) | 2.25 (1.66 to 3.04)*** | 1.97 (1.38 to 2.81)*** |
| Victim injured | 139 (5.8) | 2.20 (1.74 to 2.78)*** | 1.77 (1.30 to 2.42)*** |
| Perpetrator injured | 149 (6.2) | 2.36 (1.86 to 3.00)*** | 1.65 (1.24 to 2.21)** |
| Police involved | 130 (5.4) | 2.50 (1.94 to 3.21)*** | 1.82 (1.35 to 2.44)*** |
| Victim of violence | |||
| Intimate partner | 122 (5.1) | 5.47 (4.16 to 7.20)*** | 3.55 (2.62 to 4.82)*** |
| Family member | 44 (1.8) | 2.27 (1.47 to 3.51)*** | 1.90 (1.15 to 3.16)* |
| Friend | 76 (3.2) | 1.82 (1.29 to 2.57)*** | 1.66 (1.10 to 2.51)* |
| Known person | 128 (5.4) | 1.92 (1.49 to 2.48)*** | 1.67 (1.23 to 2.28)** |
| Stranger | 183 (7.6) | 1.76 (1.42 to 2.18)*** | 1.60 (1.22 to 2.08)** |
| Police | 26 (1.1) | 2.29 (1.34 to 3.90)** | 1.23 (0.65 to 2.32) |
| Location of violent incident | |||
| Own home | 120 (5.0) | 4.05 (3.10 to 5.29)*** | 2.80 (2.07 to 3.79)*** |
| Someone else’s home | 51 (2.1) | 3.36 (2.12 to 5.31)*** | 2.58 (1.52 to 4.38)*** |
| Bar/pub | 136 (5.7) | 2.31 (1.91 to 2.79)*** | 2.28 (1.81 to 2.88)*** |
| Street | 254 (10.6) | 1.93 (1.52 to 2.46)*** | 1.41 (1.05 to 1.90)* |
| Workplace | 19 (0.8) | 1.31 (0.73 to 2.37) | 0.98 (0.55 to 1.75) |
Significant associations were also found, following adjustments, with any phobia, obsessive–compulsive disorder, generalised anxiety disorder and mixed anxiety and depressive disorder.
When further adjusting for the same demographic factors together with alcohol and drug dependency and other psychiatric morbidity (ASPD, psychosis and depression), anxiety disorder remained significantly associated with all measures of violence except violence involving police as a victim.
Violence classes and anxiety disorder
Latent class models included five violence classes for men and three violence classes for women, as described in Chapter 2 (see Study 2).
We used multinomial logistic regression models to estimate associations between the latent classes and anxiety, which are shown for men and women in Tables 16 and 17 respectively. These revealed significant associations with anxiety disorder for all classes exhibiting violence compared with the non-violent class for both men and women after adjustments. Serious repetitive violence among men and IPV among women showed the strongest associations with anxiety disorder after adjustments.
| Violence typologies | Anxiety, n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 697 | Reference | Reference |
| Minor violence | 63 (14.0) | 1.68 (1.18 to 2.39)** | 1.54 (1.07 to 2.23)* |
| Violence towards known persons | 71 (23.9) | 2.85 (1.94 to 4.20)*** | 1.98 (1.31 to 2.99)* |
| Fighting with strangers | 72 (23.5) | 2.46 (1.67 to 3.62)*** | 1.88 (1.24 to 2.85)** |
| Serious repetitive violence | 39 (31.8) | 4.68 (2.72 to 8.04)*** | 2.63 (1.43 to 4.86)** |
| Violence typologies | Anxiety, n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 1310 (17.2) | Reference | Reference |
| General violence | 94 (33.5) | 2.18 (1.57 to 3.03)*** | 1.74 (1.23 to 2.46)** |
| Intimate/family violence | 51 (41.5) | 3.23 (2.15 to 4.84)*** | 2.87 (1.88 to 4.39)*** |
Discussion
We found a strong, independent association between anxiety disorder and violence in the general population of Great Britain. Anxiety disorder would not at first appear to be a likely risk factor for violence. It might be expected that anxious people would avoid situations leading to violence. However, a state of anxiety can also be seen as corresponding to a fight or flight response, with hyperarousal, or an acute stress response as a physiological reaction to a harmful event, attack or threat. 93 This primes the individual for fighting or fleeing. 94 Alternatively, aggression and anxiety can both result from poor emotional regulation and heightened emotional reactivity. 95 However, it is also necessary to differentiate between fear and anxiety. A behavioural inhibition system regulating responsiveness to aversive stimuli and associated with fear contrast and have differing effects on behavioural activating systems, leading to excessive emotionality, including anxiety, and subsequent violence. 96,97 The association we observed with all subcategories of anxiety disorder except for panic disorder therefore indicates the importance of a chronic state of anxiety for violence, rather than sudden extreme anxiety presenting intermittently in the form of panic attacks.
We found independent associations between anxiety disorder and all levels of violence severity. Because these findings were adjusted for other psychiatric morbidity, both the wide range of victims we observed and the levels of seriousness, including multiple incidents, confirmed the importance of a chronic state of anxiety. Anxiety disorder was also associated with each of the classes of violent individuals we identified in Chapter 2 (see Study 2). However, the strongest associations observed at the population level were for IPV, particularly among women in class 3.
These associations between anxiety disorder and IPV at the population level represent a novel finding and require further investigation. However, high levels of anxiety disorder have been observed in samples of people arrested for IPV, both men and women. 98–101 Anxiety disorder in these studies was one among several mental disorders associated with IPV. A specific role of anxiety in IPV has been described through its effects on anxious attachment in adult romantic relationships, with potentially destructive effects on relationships among highly anxious individuals. Anxious attachment is defined as uncertainty regarding the availability of attachment figures and is thought to develop when infants receive a pattern of inconsistent care from their attachment figure, becoming unsure regarding the availability of the caregiver, particularly in times of need. Children with anxious ambivalent attachment exhibit approach-avoidance behaviours towards their caregivers when distressed, mixing needs for comfort and support with emotional arousal and strong expressions of anger. People who are anxiously attached in adulthood to their partner tend to develop sexual behaviours that are less rewarding. When they are distressed, they exaggerate the severity of their adversities, become obsessed with thoughts of being abandoned by their partner and display intense negative emotions. 102,103
Intimate partner violence is thought more likely to occur when there is an inappropriate matching of partners, particularly the ‘mispairing’ of a male partner who is avoidant of making attachments with an anxious female partner, a combination thought to increase the risk of both male and female violence. 104 Avoidant men may respond to the behaviour of anxious women with violence and women then respond with violence as a self-protective behaviour. Alternatively, a woman with high attachment anxiety may view her partner’s violence as an act of rejection and respond to activation of her fears of abandonment with violence towards her partner.
Study 2: depressive disorder
Objective
The objectives of this study were to:
Methods
Sample
For the purposes of this analysis, we combined two data sets, the APMS 2007 and the NHPMS 2000, giving a total of 15,734 subjects.
Definition and assessment
Depressive symptoms were assessed using the CIS-R with depression diagnosed according to ICD-10 criteria, as described in the previous section for anxiety disorders (see Study 1). Depressive symptoms that were ongoing and which had occurred during the preceding month were included in this measure.
Associations between depression and any violence in the past 5 years were examined. Risks were measured using ORs. Weighted analyses were used to account for the sampling procedure in the surveys as described in Chapter 2.
Results
We found 441 (2.3%) people meeting criteria for depressive disorder. Table 18 shows that depression was significantly more common among those who were middle-aged, those who were single or divorced and those from lower socioeconomic groups.
| Covariate | Any violence in the past 5 years (n = 1596; 10.1%) | Depression (n = 441; 2.76%) | ||
|---|---|---|---|---|
| n (%) reported | OR (95% CI) | n (%) reported | OR (95% CI) | |
| Sex | ||||
| Female | 406 (5.1) | Reference | 253 (3.1) | Reference |
| Male | 1190 (15.4) | 3.44 (2.91 to 4.07)*** | 188 (2.4) | 0.83 (0.65 to 1.06) |
| Age group (years) | ||||
| 16–34 | 1175 (22.4) | Reference | 129 (2.4) | Reference |
| 35–54 | 371 (6.3) | 0.33 (0.28 to 0.39)*** | 217 (3.6) | 1.77 (1.33 to 2.35)** |
| 55–74 | 51 (1.1) | 0.07 (0.05 to 0.09)*** | 95 (2.0) | 1.29 (0.93 to 1.79) |
| Marital status | ||||
| Married/cohabiting | 480 (4.7) | Reference | 217 (2.1) | Reference |
| Single | 973 (23.4) | 1.76 (1.47 to 2.10)*** | 132 (4.0) | 1.63 (1.24 to 2.14)*** |
| Divorced/separated | 144 (9.9) | 2.00 (1.59 to 2.51)*** | 91 (6.2) | 2.21 (1.70 to 2.87)*** |
| Social class | ||||
| I and II | 302 (5.6) | Reference | 99 (1.8) | Reference |
| IIIM and IIINM | 708 (11.3) | 2.10 (1.74 to 2.53)*** | 180 (2.8) | 1.40 (1.08 to 1.81)** |
| IV and V | 398 (12.6) | 2.42 (1.95 to 3.01)*** | 133 (4.1) | 1.88 (1.41 to 2.50)*** |
| Ethnicity | ||||
| White | 1463 (10.2) | Reference | 386 (2.7) | Reference |
| Black | 49 (12.1) | 0.94 (0.59 to 1.50) | 18 (4.5) | 1.30 (0.72 to 2.36) |
| Indian subcontinent | 36 (7.0) | 0.46 (0.26 to 0.81)** | 15 (2.8) | 1.12 (0.57 to 2.26) |
| Other | 40 (11.2) | 0.91 (0.54 to 1.54) | 14 (3.9) | 1.49 (0.82 to 2.70) |
| ASPD | 246 (46.2) | 2.81 (2.07 to 3.82)*** | 40 (7.3) | 1.22 (0.77 to 1.93) |
| Drug dependency | 276 (48.4) | 2.31 (1.73 to 3.08)*** | 43 (7.4) | 1.53 (0.91 to 2.56) |
| Alcohol dependency | 371 (35.3) | 2.19 (1.75 to 2.74)*** | 70 (6.6) | 1.65 (1.16 to 2.35)** |
| Psychosis | 7 (14.0) | 0.52 (0.15 to 1.78) | 16 (28.4) | 2.09 (0.91 to 4.83) |
| Anxiety | 394 (16.5) | 1.90 (1.59 to 2.29)*** | 266 (10.9) | 7.42 (5.80 to 9.49)*** |
When adjusting for demographics (sex, age, marital status and ethnicity), any violence in the past 5 years remained associated with depression (OR 2.37, 95% CI 1.70 to 3.30; p < 0.001). However, when further adjusting for the same demographic factors, substance misuse and other psychiatric morbidity (anxiety, psychosis, ASPD), depression was no longer significantly associated with violence (OR 1.33, 95% CI 0.89 to 1.97; p = 0.164).
Table 19 shows the associations between depression and seriousness, victims and location of violence. After adjusting for demographics (sex, age, marital status and ethnicity), depression was associated with violence while intoxicated, repetitive violence, violence that resulted in a victim sustaining an injury, violence in which the perpetrator sustained an injury, violence that required police intervention, violence in the perpetrator’s own home or in the street, IPV and violence against a friend, known person and stranger. However, after adjusting for other psychiatric morbidity, depression remained significantly associated only with violence in the perpetrator’s own home.
| Outcomes | n (%) violent | AORa (95% CI) | AORb (95% CI) |
|---|---|---|---|
| Any violence | 79 (18.9) | 2.16 (1.66 to 2.81)*** | 1.42 (0.96 to 2.09) |
| Violence while intoxicated | 35 (8.3) | 2.08 (1.42 to 3.05)** | 1.10 (0.63 to 1.93) |
| Severity of violence | |||
| Minor violence | 25 (5.9) | 1.50 (0.92 to 2.44) | 1.28 (0.74 to 2.20) |
| Five or more violent incidents | 20 (4.6) | 2.40 (1.46 to 3.92)** | 1.09 (0.50 to 2.38) |
| Victim injured | 26 (6.1) | 2.06 (1.31 to 3.23)** | 1.09 (0.54 to 2.20) |
| Perpetrator injured | 30 (7.2) | 2.41 (1.60 to 3.61)** | 1.11 (0.59 to 2.09) |
| Police involved | 29 (6.9) | 2.81 (1.87 to 4.21)*** | 1.32 (0.75 to 2.34) |
| Victim of violence | |||
| Intimate partner | 24 (5.6) | 3.96 (2.60 to 6.02)*** | 1.24 (0.67 to 2.29) |
| Family | 7 (1.7) | 1.79 (0.84 to 3.85) | 0.89 (0.37 to 2.13) |
| Friend | 17 (4.0) | 2.14 (1.18 to 3.88)* | 1.38 (0.59 to 3.25) |
| Known person | 26 (6.2) | 2.07 (1.27 to 3.36)* | 1.07 (0.54 to 2.13) |
| Stranger | 33 (7.7) | 1.66 (1.10 to 2.51)* | 1.05 (0.62 to 1.79) |
| Police | 5 (1.2) | 2.22 (0.88 to 5.61) | 0.64 (0.22 to 1.85) |
| Location of violent incident | |||
| Own home | 35 (8.2) | 5.29 (3.60 to 7.77)*** | 2.35 (1.42 to 3.90)** |
| Someone else’s home | 5 (1.3) | 1.47 (0.74 to 2.93) | 0.62 (0.27 to 1.41) |
| Bar/pub | 24 (5.7) | 1.79 (1.10 to 2.90) | 0.85 (0.42 to 1.73) |
| Street | 48 (11.4) | 2.20 (1.56 to 3.10)*** | 1.31 (0.78 to 2.19) |
| Workplace | 7 (1.6) | 2.68 (1.08 to 6.65) | 2.20 (0.83 to 5.83) |
Violence classes and depression
Latent class models derived five violence classes for men and three violence classes for women in this joint data set as described in Chapter 2 (see Study 2). Tables 20 and 21 show the prevalence of depression among men and women in the different violence classes respectively. Class 5 in men, the serious repetitive violence class, and class 3 in women, IPV, showed the highest prevalence of depression.
| Violence typologies | Depression, n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 137 (2.1) | Reference | Reference |
| Minor violence | 10 (2.3) | 1.21 (0.50 to 2.87) | 0.94 (0.40 to 2.23) |
| Violence towards known persons | 15 (4.9) | 2.40 (1.20 to 4.78) | 1.21 (0.54 to 2.67) |
| Fighting with strangers | 9 (3.0) | 1.51 (0.64 to 3.54) | 0.86 (0.36 to 2.06) |
| Serious repetitive violence | 11 (9.4) | 4.20 (1.64 to 10.76)* | 1.63 (0.41 to 6.48) |
| Violence typologies | Depression, n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 210 (2.8) | Reference | Reference |
| General violence | 20 (7.2) | 2.46 (1.35 to 4.47)** | 1.63 (0.80 to 3.34) |
| Intimate/family violence | 13 (10.6) | 3.63 (2.09 to 6.31)*** | 2.15 (1.17 to 3.96)** |
Table 20 shows that, following adjustments for demographics, only class 5 among men (serious repetitive violence) was associated with depression. However, following further adjustments for other psychiatric morbidity, this was no longer significant. Table 21 shows the same analyses for the three violent classes in women. A strong association with depression remained among women who were violent towards intimate partners following adjustments for demographics and other psychiatric morbidity.
Discussion
This study found few independent associations between depression and violence. The findings indicate that the associations initially observed following adjustments for demography were explained by other comorbid psychopathology among depressed people who were violent. However, an independent association was observed between class 3 women who were violent towards their partners and members of their family and this remained significant following adjustments for comorbidity. This finding was supported by the only other independent association that we observed, with violence in the perpetrator’s home, corresponding to the location where violence against these victims was most likely to occur.
An epidemiological study previously found that depression was associated with an increased risk of violent behaviour in the US population. 24 However, this survey did not adjust for comorbid psychopathology. Furthermore, because no independent measure of violence was originally used at the fieldwork stage, criteria items for ASPD were used instead and ASPD could not, therefore, be used as an adjustment. Nevertheless, other epidemiological studies have argued that depression is associated with an increased risk of violent behaviour towards a spouse,105,106 in agreement with our findings. Koh et al. 107 found that patients with depression demonstrated more anger and expressions of anger than patients with anxiety disorders. However, our representative community sample suggested that the association with anxiety disorder was stronger than the association with depressive disorder when the outcome was actual violence.
These findings do not support the psychoanalytical theory that conflicts about anger play a central role in the development of depression. Anger in people with depression is thought to stem from narcissistic vulnerability, a sensitivity to perceived or actual loss or rejection. These angry reactions are then thought to cause intrapsychic conflicts through the onset of guilt and fear that angry feelings will disrupt relationships. These conflicts lead to anger being directed inwards and further lowered self-esteem, creating a vicious cycle. Defence mechanisms that are triggered are ineffective in managing these conflicts and are thought to further prevent the appropriate expression of anger. 108 Certain features of this psychodynamic formulation show some correspondence with our observations for IPV, but for anxiety disorder rather than depression (see Study 1, Discussion). Furthermore, the anger (or proneness to violence) thought to stem from a narcissistic vulnerability in this study could be considered a comorbid form of psychopathology, such as the narcissistic personality components of ASPD, as the key factor leading to violence, and not depression.
Study 3: psychosis
Objectives
The objectives of this study were to investigate:
-
the prevalence of violent behaviour in a large representative sample of the adult population of Great Britain associated with psychosis and symptoms of psychosis
-
the independent associations between psychosis and psychotic symptoms and characteristics of violence, victim types and locations of violence
-
the independent associations between psychosis and symptoms of psychosis and the classes of violence identified in the population.
Methods
Sample
For the purposes of this analysis we combined two data sets, the NHPMS 2000 and the APMS 2007, to provide a total of 15,734 subjects.
The PSQ was used to assess the experience of five common symptoms of psychosis: hypomania, thought interference, paranoid delusions, strange experiences and auditory and visual hallucinations. 51 We used a cut-off point of three or more symptoms using the PSQ to identify a categorical diagnosis of psychosis.
Statistical analyses
Weighted (n) frequencies and proportions were reported on all categorical variables. Group associations between psychosis and violence were established using binary logistic regression with the OR as the measure of magnitude. Multiple categorical predictor covariates were assigned a reference category against which other categories were contrasted.
All statistical analyses were adjusted by including covariates in each model simultaneously. Adjustments were carried out for demographic factors, drug dependency, alcohol dependency, ASPD and anxiety disorders. To adjust for the effects of selecting one individual per household and the under-representation of certain subgroups, and to account for any deviation from selecting a simple random sample, all estimates were weighted. Details of the procedures used in weighting have previously been described. 45 All models employed robust SEs to adjust for clustering of individuals within postcodes. All analyses were performed using Stata version 13 (StataCorp LP, College Station, TX, USA).
Results
Demographic characteristics
Of 15,743 respondents, 1596 (10.1%) reported violence in the past 5 years. Table 22 shows that sociodemographic factors of male sex, marital status other than married and social class lower than I and II were significantly associated with violence, whereas age > 34 years was protective. The weighted count and prevalence of psychosis in this joint sample was 55 (0.34%). Separated marital status and black ethnicity were demographic factors associated with an increased risk of psychosis. Anxiety disorder was significantly associated with psychosis in adjusted models.
| Covariate | Any violence in the past 5 years (n = 1596; 10.1%) | Psychosis (n = 55; 0.3%) | ||
|---|---|---|---|---|
| n (%) | AORa (95% CI) | n (%) | AORa (95% CI) | |
| Sex | ||||
| Female | 406 (5.1) | Reference | 28 (0.4) | Reference |
| Male | 1190 (15.4) | 3.41 (2.89 to 4.04)*** | 27 (0.3) | 0.99 (0.52 to 1.89) |
| Age group (years) | ||||
| 16–34 | 1175 (22.4) | Reference | 20 (0.4) | Reference |
| 35–54 | 371 (6.3) | 0.33 (0.28 to 0.39)*** | 28 (0.5) | 3.35 (1.02 to 10.98)* |
| ≥ 55 | 51 (1.1) | 0.06 (0.05 to 0.09)*** | 8 (0.2) | 2.18 (0.51 to 9.43) |
| Marital status | ||||
| Married/cohabiting | 480 (4.7) | Reference | 19 (0.2) | Reference |
| Single | 973 (23.4) | 1.80 (1.51 to 2.14)*** | 22 (0.5) | 2.10 (0.68 to 6.49) |
| Separated/divorced | 144 (9.9) | 2.03 (1.62 to 2.56)*** | 14 (1.0) | 3.90 (1.81 to 8.40)*** |
| Social class | ||||
| I and II | 302 (5.6) | Reference | 12 (0.2) | Reference |
| IIIM and IIINM | 708 (11.3) | 2.11 (1.76 to 2.54)*** | 10 (0.2) | 0.72 (0.31 to 1.68) |
| IV and V | 398 (12.6) | 2.41 (1.94 to 3.00)*** | 19 (0.6) | 1.76 (0.77 to 4.02) |
| Ethnicity | ||||
| White | 1463 (10.2) | Reference | 36 (0.3) | Reference |
| Black | 49 (12.1) | 0.91 (0.57 to 1.44) | 12 (2.8) | 8.34 (3.29 to 21.13)*** |
| Indian subcontinent | 36 (7.0) | 0.46 (0.26 to 0.81)** | 4 (0.8) | 2.14 (0.62 to 7.39) |
| Other | 40 (11.2) | 0.91 (0.54 to 1.52) | 0 (0.0) | Collapsed |
| Drug dependency | 276 (48.4) | 2.32 (1.75 to 3.09)*** | 8 (1.5) | 1.46 (0.44 to 4.85) |
| Alcohol dependency | 371 (35.3) | 2.20 (1.76 to 2.75)*** | 10 (1.0) | 1.75 (0.60 to 5.09) |
| Anxiety disorder | 375 (16.2) | 1.86 (1.55 to 2.24)*** | 39 (1.6) | 10.74 (4.89 to 23.57)*** |
| ASPD | 246 (46.2) | 2.85 (2.10 to 3.87)*** | 5 (1.0) | 1.25 (0.37 to 4.24) |
Associations of psychosis with violence
Table 23 shows the unadjusted and adjusted associations of psychosis with level of severity of violence, victim types and locations of reported violence. Before adjustment, psychosis as a categorical construct showed associations only with violence towards a friend and in an unspecified location. After adjustments there were no associations between psychosis and violence in the household population.
| Outcomes | n (%) violent | OR (CI 95%) | AORa (CI 95%) |
|---|---|---|---|
| Any violence | 1596 (10.1) | 1.54 (0.61 to 3.86) | 0.54 (0.16 to 1.84) |
| Violence while intoxicated | 685 (4.3) | 1.44 (0.46 to 4.51) | 0.56 (0.19 to 1.61) |
| Severity of violence | |||
| Minor violence | 655 (4.2) | 1.10 (0.21 to 5.67) | 0.21 (0.03 to 1.60) |
| Five or more violent incidents | 335 (2.1) | 1.71 (0.53 to 5.54) | 1.49 (0.50 to 4.48) |
| Victim injured | 505 (3.2) | 1.70 (0.47 to 6.21) | 0.62 (0.14 to 2.76) |
| Perpetrator injured | 514 (3.3) | 1.59 (0.44 to 5.81) | 0.49 (0.13 to 1.90) |
| Police involved | 431 (2.7) | 2.12 (0.55 to 8.22) | 1.60 (0.34 to 7.49) |
| Victim of violence | |||
| Intimate partner | 252 (1.6) | 1.17 (0.28 to 4.92) | 0.46 (0.11 to 1.83) |
| Family member | 153 (1.0) | 1.00 (1.00 to 1.00) | 1.00 (1.00 to 1.00) |
| Friend | 312 (2.0) | 3.89 (1.07 to 14.20)* | 0.58 (0.10 to 3.37) |
| Person known | 511 (3.2) | 1.19 (0.24 to 5.76) | 0.23 (0.04 to 1.31) |
| Stranger | 783 (5.0) | 1.55 (0.44 to 5.51) | 1.08 (0.19 to 6.27) |
| Police | 88 (0.6) | 2.22 (0.30 to 16.33) | 1.69 (0.18 to 15.64) |
| Other | 108 (0.7) | 1.79 (0.24 to 13.16) | 2.54 (0.33 to 19.30) |
| Location of violent incident | |||
| Own home | 292 (1.9) | 1.23 (0.29 to 5.15) | 0.56 (0.15 to 2.07) |
| Someone else’s home | 138 (0.9) | 4.19 (0.84 to 20.98) | 0.55 (0.07 to 4.69) |
| Street | 909 (5.8) | 1.51 (0.48 to 4.77) | 0.87 (0.20 to 3.91) |
| Bar/pub | 541 (3.4) | 1.91 (0.61 to 6.01) | 0.81 (0.29 to 2.26) |
| Workplace | 101 (0.6) | 2.27 (0.31 to 16.70) | 1.59 (0.23 to 11.12) |
| Other | 241 (1.5) | 5.37 (1.51 to 19.15)** | 1.35 (0.16 to 11.30) |
Individual psychotic symptoms and characteristics of violence
Analyses of the associations between all violent outcomes and each psychotic symptom in the PSQ were carried out (Table 24). There were no associations between hypomania and violence. Thought insertion significantly increased the risk for any reported violence, violence in which the police were involved and violence towards a stranger. The paranoid delusions item showed the largest number of associations – with any violence, violence when intoxicated, five or more violent incidents, victim injured, violence against a known person, violence occurring in the street and violence occurring in a bar or pub. Strange experiences doubled the risk of IPV. Hallucinations were not associated with any of the violent outcomes.
| Outcomes | Hypomania, AORa (95% CI) | Thought insertion, AORa (95% CI) | Paranoid delusions, AORa (95% CI) | Strange experiences, AORa (95% CI) | Hallucinations, AORa (95% CI) |
|---|---|---|---|---|---|
| Any violence | 0.97 (0.37 to 2.56) | 1.85 (1.05 to 3.26)* | 1.75 (1.03 to 2.96)* | 1.03 (0.68 to 1.56) | 0.92 (0.48 to 1.74) |
| Violence while intoxicated | 0.20 (0.02 to 2.23) | 1.50 (0.64 to 3.47) | 1.90 (1.07 to 3.35)* | 1.12 (0.65 to 1.95) | 1.46 (0.63 to 3.34) |
| Severity of violence | |||||
| Minor violence | 0.76 (0.16 to 3.65) | 1.51 (0.68 to 3.38) | 1.03 (0.50 to 2.12) | 0.69 (0.37 to 1.30) | 0.55 (0.16 to 1.87) |
| Five or more violent incidents | 0.58 (0.08 to 4.16) | 1.60 (0.73 to 3.51) | 3.59 (1.83 to 7.05)*** | 0.55 (0.29 to 1.05) | 2.25 (0.89 to 5.72) |
| Victim injured | 1.63 (0.45 to 5.81) | 0.69 (0.20 to 2.33) | 2.15 (1.13 to 4.09)* | 0.97 (0.50 to 1.87 | 1.34 (0.51 to 3.47) |
| Perpetrator injured | 1.65 (0.57 to 4.77) | 1.29 (0.53 to 3.17) | 1.42 (0.77 to 2.63) | 0.91 (0.51 to 1.62) | 1.83 (0.87 to 3.86) |
| Police involved | 1.25 (0.34 to 4.57) | 3.07 (1.44 to 6.56)** | 1.36 (0.72 to 2.57) | 1.43 (0.80 to 2.55) | 1.21 (0.53 to 2.77) |
| Victim of violence | |||||
| Intimate partner | 1.26 (0.39 to 4.03) | 1.16 (0.51 to 2.64) | 1.44 (0.69 to 2.99) | 2.04 (1.08 to 3.85)* | 0.82 (0.28 to 2.36) |
| Family member | 1.57 (0.29 to 8.32) | 2.71 (0.85 to 8.61) | 1.41 (0.55 to 3.63) | 1.16 (0.41 to 3.29) | 2.40 (0.72 to 7.97) |
| Friend | 1.41 (0.16 to 12.22) | 1.12 (0.32 to 3.90) | 2.01 (0.83 to 4.87) | 1.17 (0.54 to 2.52) | 0.76 (0.18 to 3.13) |
| Person known | 0.93 (0.16 to 5.52) | 0.64 (0.22 to 1.83) | 1.96 (1.05 to 3.65)* | 0.79 (0.40 to 1.54) | 1.10 (0.40 to 2.99) |
| Stranger | 0.67 (0.17 to 2.65) | 2.91 (1.31 to 6.45)** | 1.45 (0.79 to 2.68) | 0.82 (0.48 to 1.39) | 1.27 (0.58 to 2.79) |
| Police | 2.32 (0.25 to 21.56) | 3.32 (0.71 to 15.49) | 1.50 (0.47 to 4.83) | 1.28 (0.46 to 3.54) | 2.25 (0.66 to 7.73) |
| Other | 1.67 (0.19 to 14.25) | 3.17 (0.81 to 12.36) | 2.75 (0.83 to 9.06) | 0.90 (0.28 to 2.92) | 1.05 (0.15 to 7.21) |
| Location of violent incident | |||||
| Own home | 1.80 (0.61 to 5.34) | 1.35 (0.63 to 2.92) | 0.99 (0.50 to 1.96) | 1.19 (0.65 to 2.18) | 1.82 (0.73 to 4.54) |
| Someone else’s home | 0.62 (0.08 to 5.05) | 1.45 (0.34 to 6.23) | 1.68 (0.58 to 4.85) | 0.84 (0.31 to 2.27) | 1.09 (0.24 to 4.94) |
| Street | 1.12 (0.33 to 3.74) | 1.03 (0.46 to 2.30) | 1.84 (1.00 to 3.37)* | 0.90 (0.55 to 1.47) | 1.51 (0.72 to 3.13) |
| Bar/pub | 0.57 (0.12 to 2.78) | 1.20 (0.44 to 3.28) | 2.22 (1.25 to 3.95)** | 0.86 (0.46 to 1.63) | 0.89 (0.33 to 2.41) |
| Workplace | 1.24 (0.15 to 10.54) | 0.75 (0.17 to 3.34) | 1.26 (0.26 to 6.16) | 0.77 (0.26 to 2.30) | 1.57 (0.38 to 6.41) |
| Other | 1.00 (1.00 to 1.00) | 3.56 (0.97 to 13.09) | 3.09 (1.21 to 7.86)* | 0.53 (0.21 to 1.37) | 1.73 (0.49 to 6.10) |
Violence classes and psychosis
Latent class models derived five violence classes for men and three violence classes for women (see Chapter 2, Study 2). Tables 25 and 26 show the associations between these classes and psychosis for men and women respectively.
| Violence typologies | Psychosis, n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 22 (0.3) | Reference | Reference |
| Minor violence | 2 (0.4) | 0 (0 to 0) | 0 (0 to 0) |
| Violence towards known persons | 1 (0.2) | 0.70 (0.10 to 5.03) | 0.43 (0.06 to 2.80) |
| Fighting with strangers | 2 (0.6) | 3.33 (0.37 to 29.63) | 2.15 (0.25 to 18.49) |
| Serious repetitive violence | 1 (0.5) | 3.36 (0.35 to 31.89) | 2.03 (0.17 to 23.92) |
| Violence typologies | Psychosis, n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 24 (0.3) | Reference | Reference |
| General violence | 2 (0.8) | 1.22 (0.24 to 6.29) | 0.62 (0.13 to 3.01) |
| Intimate/family violence | 0 (0.0) | 0 (0 to 0) | 0 (0 to 0) |
The ‘no violence’ class had the lowest prevalence of psychosis (0.3%). Prevalences were < 1% for all classes and only marginally increased above the population base rate (0.3%). No significant associations were found between the latent classes and psychosis.
Violence typologies and individual psychotic symptoms
The frequencies and percentages of individual psychotic symptoms observed for each violence typology class are provided in Table 27 for men and Table 28 for women.
| Psychotic symptoms (PSQ)a | Class | ||||
|---|---|---|---|---|---|
| No violence, n (%) | Minor violence, n (%) | Violence towards known persons, n (%) | Fighting with strangers, n (%) | Serious repetitive violence, n (%) | |
| Hypomania, 0.7% | 43 (0.7) | 5 (1.0) | 1 (0.5) | 3 (0.9) | 0 (0.0) |
| Thought insertion, 0.9% | 55 (0.8) | 4 (1.0) | 4 (1.2) | 6 (2.1) | 3 (2.4) |
| Paranoid delusions, 2.1% | 95 (1.4) | 14 (3.1) | 20 (6.9) | 12 (3.8) | 20 (16.7) |
| Strange experiences, 3.0% | 165 (2.5) | 14 (3.2) | 15 (5.0) | 22 (7.0) | 21 (16.9) |
| Hallucinations, 0.8% | 42 (0.6) | 4 (0.9) | 2 (0.6) | 7 (2.4) | 5 (4.5) |
| Psychotic symptoms (PSQ)a | Class | ||
|---|---|---|---|
| No violence, n (%) | General violence, n (%) | Intimate/family violence, n (%) | |
| Hypomania, 0.6% | 38 (0.5) | 6 (2.2) | 3 (2.3) |
| Thought insertion, 1.0% | 66 (0.9) | 11 (3.9) | 4 (3.3) |
| Paranoid delusions, 1.4% | 86 (1.1) | 20 (7.0) | 2 (1.6) |
| Strange experiences, 3.3% | 216 (2.8) | 27 (9.6) | 13 (10.4) |
| Hallucinations, 1.0% | 74 (1.0) | 5 (1.9) | 2 (1.3) |
In men, both ‘paranoid delusions’ and ‘strange experiences’ appear over-represented in the classes ‘violence towards known persons’ and ‘serious repetitive violence’. Association tests after adjusting for the other psychotic symptoms showed that ‘paranoid delusions’ was the only psychotic symptom associated with any violence classes, namely ‘violence towards known persons’ and ‘serious repetitive violence’ (Table 29).
| Psychotic symptoms (PSQ) | Class | ||||
|---|---|---|---|---|---|
| No violence, RRRa (95% CI) | Minor violence, RRRa (95% CI) | Violence towards known persons, RRRa (95% CI) | Fighting with strangers, RRRa (95% CI) | Serious repetitive violence, RRRa (95% CI) | |
| Hypomania | Reference | 0.88 (0.12 to 6.32) | 0.73 (0.10 to 5.37) | 1.18 (0.25 to 5.48) | 0 (0 to 0) |
| Adjustedb | Reference | 0.76 (0.09 to 6.19) | 0.55 (0.05 to 5.52) | 0.99 (0.19 to 5.03) | 0 (0 to 0) |
| Thought insertion | Reference | 1.46 (0.54 to 3.96) | 1.44 (0.47 to 4.43) | 2.67 (0.89 to 8.01) | 5.23 (1.28 to 21.32)* |
| Adjustedb | Reference | 1.61 (0.49 to 5.29) | 1.14 (0.35 to 3.74) | 1.35 (0.43 to 4.26) | 2.06 (0.48 to 8.76) |
| Paranoid delusions | Reference | 1.77 (0.74 to 4.25) | 4.43 (2.14 to 9.16)*** | 2.57 (1.15 to 5.75)* | 15.22 (6.57 to 35.27)*** |
| Adjustedb | Reference | 1.43 (0.53 to 3.88) | 2.69 (1.16 to 6.25)* | 1.06 (0.46 to 2.46) | 4.68 (1.62 to 13.51)** |
| Strange experiences | Reference | 0.84 (0.38 to 1.88) | 0.94 (0.43 to 2.03) | 2.67 (1.47 to 4.84)** | 5.26 (2.56 to 10.78)*** |
| Adjustedb | Reference | 0.58 (0.24 to 1.42) | 0.39 (0.15 to 1.00) | 1.33 (0.68 to 2.60) | 1.20 (0.45 to 3.21) |
| Hallucinations | Reference | 0.46 (0.06 to 3.62) | 0.88 (0.17 to 4.53) | 3.93 (1.46 to 10.53)** | 7.00 (1.50 to 32.64)* |
| Adjustedb | Reference | 0.37 (0.04 to 3.18) | 0.54 (0.08 to 3.59) | 2.29 (0.86 to 6.08) | 2.18 (0.43 to 11.12) |
For women, the most salient psychotic symptom observed in the classes ‘general violence’ and ‘intimate/family violence’ was ‘strange experiences’ (see Table 28). However, after adjusting for the other psychotic symptoms, only ‘thought insertion’ significantly increased the likelihood of membership of the class ‘general violence’ (Table 30).
| Psychotic symptoms (PSQ) | Class | ||
|---|---|---|---|
| No violence, RRRa (95% CI) | General violence, RRRa (95% CI) | Intimate/family violence, RRRa (95% CI) | |
| Hypomania | Reference | 1.21 (0.23 to 6.47) | 3.43 (1.06 to 11.07)* |
| Adjustedb | Reference | 1.20 (0.21 to 6.75) | 3.08 (0.93 to 10.22) |
| Thought insertion | Reference | 4.94 (2.33 to 10.47)*** | 3.32 (1.23 to 8.91)* |
| Adjustedb | Reference | 3.73 (1.54 to 9.07)** | 2.63 (0.89 to 7.77) |
| Paranoid delusions | Reference | 4.95 (2.40 to 10.19)*** | 1.20 (0.44 to 3.30) |
| Adjustedb | Reference | 2.31 (0.87 to 6.11) | 0.45 (0.13 to 1.55) |
| Strange experiences | Reference | 2.54 (1.43 to 4.53)** | 3.29 (1.59 to 6.81)** |
| Adjustedb | Reference | 1.24 (0.56 to 2.77) | 2.13 (0.89 to 5.12) |
| Hallucinations | Reference | 1.41 (0.52 to 3.80) | 1.20 (0.27 to 5.38) |
| Adjustedb | Reference | 0.43 (0.13 to 1.42) | 0.44 (0.09 to 2.25) |
Discussion
Epidemiological studies have demonstrated that the key risk factors for violence among those with psychosis are the same as those among the general population, after adjustment for comorbid psychopathology. 83,109–112 Considerable emphasis has been placed on the possibility that most of the associations (or indeed the entire association) between psychotic illness and violence are the result of co-occurring substance misuse disorder. 88,110 We did not find a significant association between violence in general in the population and our categorical measure of psychosis after adjustments, including for substance dependence. Prior to adjustments we found an association with violence towards friends and violence in ‘other’ locations, but these associations were no longer present following adjustments. Furthermore, there was considerable change in the odds of association and, although no longer significant, there was a trend for psychosis to have a negative association with several violent outcomes.
Examining the CIs, the power to detect an association in this sample was low. Only 0.3% of the population was classified as psychotic using the PSQ. This is most likely because we chose a high cut-off point of three out of five PSQ items. We have previously investigated the association in the 2000-household sample using a lower cut-off point of two or more items. 83 However, the prevalence of those screening positive was still lower than might be expected at 0.6%. Furthermore, after adjustments in this previous study, we did not find associations with any of the outcomes, including levels of violence severity, victim types or locations of the violence, except for a significant association with five or more violent incidents. 30 Taken together, our current findings would tend to confirm our previous findings that comorbid psychopathology is the explanatory variable in a pathway between a categorical diagnosis of psychotic illness and violence.
More recently, several researchers have begun to reconsider the association between violence and psychosis and the observation that the risk of violence is higher when the symptoms of the psychosis are active. 113–119 However, associations between psychotic symptoms and violence at the population level have not been firmly established. Earlier studies challenged the classical ‘syndrome’ approach and promoted the investigation of single symptoms. 120,121 By investigating individual symptoms, we confirmed a much stronger association with violence, but only for symptoms of thought insertion, strange experiences and paranoid delusions. In general, the strongest odds of association and the largest number of outcomes were observed in the associations between violence and paranoid delusions. Paranoid delusions were associated with violence when intoxicated, repetitive violence (five or more incidents) and injury to a victim. There was a specific association with persons known and these incidents were more likely to occur in the street and in a bar/pub as well as in ‘other’ locations. Paranoid delusions were also independently associated with membership of class 5 for men (‘serious repetitive violence’) and class 3 for women (‘intimate/family violence’).
Our findings therefore correspond to those of previous studies showing that, among a range of psychotic symptoms, persecutory delusions are most strongly associated with violence in community surveys. 111,122–124 However, the findings relating to hallucinations, thought interference and external influences or control were inconsistent in these studies. The considerable methodological heterogeneity of studies, previously observed in meta-analyses of clinical psychosis, is likely to explain this. 91
Among the other symptoms, we found that thought insertion had an independent association with any violence, violent incidents that were serious enough for the police to become involved and violent incidents involving a stranger. There was no association between the violence classes among men and thought insertion. However, this symptom was associated with the general violence class (class 2) among women.
Strange experiences did not show an association with any of the levels of violence severity or locations. This symptom was found to show an association only with IPV. There were also no associations between strange experiences and the classes of violence for men or for women in this sample.
Chapter 4 Personality disorders
Background
Most mental health professionals believe that there is a strong relationship between personality disorders and violence. In the past, this has led to reluctance to accept patients with this diagnosis. 125 Longitudinal studies have provided strong evidence of personality disorders representing a significant risk for future violence. 30,126,127 Personality disorder symptoms were found to be even stronger predictors of violence than overall diagnosis. In particular, items from personality disorders included in cluster A and cluster B disorders corresponded to violence in the community. Overall, paranoid, narcissistic, passive–aggressive personality disorder symptoms correlated significantly with violence. These findings remained significant after controlling for Axis I disorders and demography. However, it was of significant interest that these researchers did not find any associations between violence and BPD.
These findings were generally confirmed in a survey of adults in households in Great Britain, with those with cluster B disorders (including ASPD, BPD and narcissistic personality disorder) being 10 times as likely to be violent as those without. 30 However, it is probable that a single cluster B disorder, ASPD, had primarily accounted for the raised risk. One possibility considered by previous researchers is that clinical and forensic studies that have a high prevalence of BPD are likely to have sampled a more severe form of this condition than is studied in community samples. An elevated risk for violence might be evident only in prisoners and in hospitalised patients with severe conditions. These are typically characterised by poor emotional regulation and impulse control leading to behaviour that in turn results in hospitalisation. 126,127 Nevertheless, Roberts and Coid128 did not find an association between violent offending and BPD in a representative sample of UK prisoners. Conduct disorder and adult antisocial symptoms using continuous scores demonstrated the highest and most frequent associations with criminal behaviour including violence. ASPD was thought to demonstrate extensive overlap with the criminological construct of the career criminal. 129,130 Previous studies of offending behaviour among individuals with ASPD have consistently shown an association with crimes involving financial gain, including burglary and theft, robbery and firearm offences,131 and violent offences. 132–134
It is unsurprising that ASPD is found to be associated with violence because certain criteria for diagnosing ASPD actually include violent behaviour. The essential feature of ASPD is a pervasive pattern of disregard for, and violation of, the rights of others that begins in childhood or early adolescence and continues into adulthood. For the diagnosis to be given, the individual must be at least 18 years of age and have had a history of some symptoms of conduct disorder before the age of 15 years. Conduct disorder involves a repetitive and persistent pattern of behaviour in which the basic rights of others or major age-appropriate societal norms or rules are violated. 135 BPD is defined as a pervasive pattern of instability of interpersonal relationships, self-image and affects and marked impulsivity, beginning by early adulthood and present in a variety of contexts. 135 It might be expected that criteria for BPD, which include unstable and intense interpersonal relationships, affective instability because of a marked reactivity of mood, and inappropriate, intense anger or difficulty controlling anger, might be associated with violent behaviour despite previous studies failing to find this association. In this chapter we report an investigation of the associations of ASPD and BPD with violence in a large representative sample of the population of Great Britain. Although the DSM-IV Axis II Personality Disorders (SCID-II) screening questionnaire includes 10 personality disorder categories, only ASPD and BPD have consistently been included in the National Household Surveys. Furthermore, these two personality disorders are the only personality disorders that are currently within National Institute for Health and Care Excellence (NICE) guidelines for treatment and management. 136,137
Study 1: antisocial personality disorder
Objectives
The objectives of the study were to investigate:
-
the prevalence of self-reported violence associated with ASPD in a large representative sample of the household population of Great Britain
-
the independent associations of ASPD with different characteristics of violence, victim types and locations of violent behaviour
-
the association between ASPD and comorbid psychiatric disorder and violence
-
the association between ASPD and latent classes of violence among men and women.
Methods
Sample
For the purpose of this analysis we combined two data sets, the NHPMS 2000 and the APMS 2007, to provide a total of 15,734 subjects.
Antisocial personality disorder was identified using the SCID-II screen. 53 Participants gave ‘yes’ or ‘no’ responses to questions administered on a laptop computer. The ASPD category of the Diagnostic and Statistical Manual of Mental Disorders, Fourth edition (DSM-IV) Axis II138 was created by manipulating cut-off points to increase levels of agreement, measured by the kappa coefficient, between both individual criteria and clinical diagnoses. 52
Analysis
Weighted (n) frequencies and proportions were reported on all categorical variables. Group associations between ASPD and violence were established using binary logistic regression with the OR as the measure of magnitude. Multiple categorical predictor covariates were assigned a reference category against which other categories were contrasted.
All statistical analyses were adjusted by including covariates in each model simultaneously. Adjustments were carried out for demographic factors, depression, drug dependency, alcohol dependency, screening positive for psychosis (three or more symptoms on the PSQ) and anxiety disorders. To adjust for the effects of selecting one individual per household and under-representation of certain subgroups, and to account for any deviation from selecting a simple random sample, all estimates were weighted. Details of the procedures used in weighting have been described previously. 45,82 All models employed robust SEs to adjust for clustering of individuals within postcodes. All analyses were performed using Stata.
Results
Demographic characteristics
Of the 15,734 respondents, 1596 (10.1%) reported any violence in the past 5 years. Table 31 shows that male sex, marital status other than married and social class lower than I and II were significantly associated with violence, whereas age > 34 years was protective. The overall prevalence of ASPD in the sample was 3.4%. The older age group (≥ 55 years) showed a significantly lower rate of ASPD. Being male, separated or divorced and from social classes IV and V showed significantly increased associations with ASPD. Anxiety disorders and drug and alcohol dependency were also significantly associated with ASPD in adjusted models.
| Covariate | Any violence in the past 5 years (n = 1596; 10.1%) | ASPD (n = 542; 3.4%) | ||
|---|---|---|---|---|
| n (%) | AORa (95% CI) | n (%) | AORa (95% CI) | |
| Sex | ||||
| Female | 406 (5.1) | Reference | 127 (1.6) | Reference |
| Male | 1190 (15.4) | 3.57 (3.02 to 4.21)*** | 414 (5.3) | 3.30 (2.52 to 4.31)*** |
| Age group (years) | ||||
| 16–34 | 1175 (22.4) | Reference | 320 (6.1) | Reference |
| 35–54 | 371 (6.3) | 0.33 (0.28 to 0.39)*** | 182 (3.1) | 0.67 (0.51 to 0.90)** |
| ≥ 55 | 51 (1.1) | 0.06 (0.05 to 0.08)*** | 40 (0.9) | 0.25 (0.17 to 0.36)*** |
| Marital status | ||||
| Married/cohabiting | 480 (4.7) | Reference | 209 (2.1) | Reference |
| Single | 973 (23.4) | 1.78 (1.49 to 2.13)*** | 265 (6.3) | 1.26 (0.92 to 1.72) |
| Separated/divorced | 144 (9.9) | 2.09 (1.66 to 2.63)*** | 68 (4.7) | 1.77 (1.29 to 2.43)*** |
| Social class | ||||
| I and II | 302 (5.6) | Reference | 139 (2.6) | Reference |
| IIIM and IIINM | 708 (11.3) | 2.11 (1.76 to 2.53)*** | 227 (3.6) | 1.24 (0.95 to 1.61) |
| IV and V | 398 (12.6) | 2.47 (1.99 to 3.06)*** | 147 (4.6) | 1.60 (1.18 to 2.17)** |
| Ethnicity | ||||
| White | 1463 (10.2) | Reference | 496 (3.4) | Reference |
| Black | 49 (12.1) | 0.90 (0.57 to 1.44) | 14 (3.5) | 0.90 (0.47 to 1.73) |
| Indian subcontinent | 36 (7.0) | 0.44 (0.25 to 0.78)** | 9 (1.8) | 0.47 (0.18 to 1.26) |
| Other | 40 (11.2) | 0.90 (0.54 to 1.51) | 17 (4.6) | 1.19 (0.56 to 2.52) |
| Drug dependency | 276 (48.4) | 2.93 (2.22 to 3.86)*** | 159 (27.8) | 6.12 (4.32 to 8.67)*** |
| Alcohol dependency | 371 (35.3) | 2.38 (1.91 to 2.96)*** | 170 (16.1) | 2.47 (1.80 to 3.39)*** |
| Anxiety disorder | 375 (16.2) | 2.00 (1.67 to 2.40)*** | 190 (8.1) | 2.61 (2.00 to 3.40)*** |
| Psychosis | 7 (14.0) | 0.52 (0.16 to 1.70) | 5 (10.7) | 1.20 (0.36 to 3.99) |
Main associations of antisocial personality disorder with violence
Table 32 shows unadjusted and adjusted associations of ASPD with violent outcomes, victim types and locations of reported violence. The only violent outcomes that ASPD did not show associations with following adjustments were minor violence, violence towards a friend and violence occurring in someone else’s home.
| Outcomes | n (%) | OR (CI 95%) | AORa (CI 95%) |
|---|---|---|---|
| Any violence | 1596 (10.1) | 8.57 (6.89 to 10.66)*** | 2.81 (2.07 to 3.82)*** |
| Violence while intoxicated | 685 (4.3) | 11.40 (8.82 to14.74)*** | 3.14 (2.21 to 4.45)*** |
| Severity of violence | |||
| Minor violence | 655 (4.2) | 2.52 (1.81 to 3.52)*** | 1.03 (0.67 to 1.58) |
| Five or more violent incidents | 335 (2.1) | 10.54 (7.48 to 14.84)*** | 2.77 (1.75 to 4.37)*** |
| Victim injured | 505 (3.2) | 12.19 (9.23 to 16.11)*** | 3.98 (2.74 to 5.77)*** |
| Perpetrator injured | 514 (3.3) | 9.93 (7.50 to 13.14)*** | 3.04 (2.08 to 4.45)*** |
| Police involved | 431 (2.7) | 10.44 (7.72 to 14.11)*** | 3.17 (2.19 to 4.60)*** |
| Victim of violence | |||
| Intimate partner | 252 (1.6) | 9.99 (7.02 to 14.23)*** | 3.98 (2.40 to 6.60)*** |
| Family member | 153 (1.0) | 6.80 (4.00 to 11.56)*** | 3.08 (1.66 to 5.71)*** |
| Friend | 312 (2.0) | 5.68 (3.83 to 8.42)*** | 1.40 (0.83 to 2.35) |
| Person known | 511 (3.2) | 8.17 (6.01 to 11.10)*** | 2.52 (1.65 to 3.84)*** |
| Stranger | 783 (5.0) | 7.36 (5.62 to 9.64)*** | 2.16 (1.54 to 3.04)*** |
| Police | 88 (0.6) | 26.07(15.63 to 43.48)*** | 7.69 (4.25 to13.90)*** |
| Other | 108 (0.7) | 7.49 (4.23 to 13.26)*** | 3.24 (1.43 to 7.32)** |
| Location of violent incident | |||
| Own home | 292 (1.9) | 6.85 (4.71 to 9.96)*** | 2.73 (1.63 to 4.55)*** |
| Someone else’s home | 138 (0.9) | 7.05 (4.05 to 12.28)*** | 2.15 (1.00 to 4.65) |
| Street | 909 (5.8) | 8.64 (6.78 to 11.01)*** | 2.60 (1.87 to 3.62)*** |
| Bar/pub | 541 (3.4) | 10.29 (7.75 to 13.67)*** | 2.90 (2.02 to 4.16)*** |
| Workplace | 101 (0.6) | 8.58 (4.90 to 15.05*** | 3.47 (1.90 to 6.32)*** |
| Other | 241 (1.5) | 6.23 (3.93 to 9.89)*** | 2.03 (1.12 to 3.66)* |
Impact of coexisting disorders on the association of antisocial personality disorder with any violence
We performed logistic regression analyses stratified by comorbid categories of psychiatric morbidity (presence/absence) to examine the impact of specific co-occurring disorders on the association of ASPD with any reported violence. Table 33 shows the results of these adjusted analyses. ASPD was significantly associated with any violence both in the absence and in the presence of comorbid drug dependency, alcohol dependency, psychosis and anxiety disorders. The odds of association for violence and APSD in the presence of each co-occurring disorder were higher than the odds in the absence of the co-occurring disorder. However, these differences were not statistically significant.
| Comorbid category | Without comorbidity | With comorbidity |
|---|---|---|
| Psychosis | 2.81 (2.07 to 3.82)*** | 5.46 (1.62 to 18.35)** |
| Drug dependency | 2.66 (1.88 to 3.77)*** | 5.55 (3.24 to 9.52)*** |
| Alcohol dependency | 2.79 (1.97 to 3.96)*** | 3.72 (2.24 to 6.18)*** |
| Anxiety disorder | 2.85 (1.95 to 4.19)*** | 3.50 (2.27 to 5.42)*** |
Violence classes and antisocial personality disorder
Latent class models derived five violence classes for men and three violence classes for women in this joint data set (see Chapter 2, Study 2). Tables 34 and 35 show the associations between individuals with ASPD and the violence classes for men and for women respectively.
| Violence typologies | ASPD, n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 223 (3.4) | Reference | Reference |
| Minor violence | 34 (7.6) | 1.76 (1.10 to 2.81)* | 1.35 (0.81 to 2.24) |
| Violence towards known persons | 55 (18.6) | 4.26 (2.75 to 6.62)*** | 2.33 (1.37 to 3.98)** |
| Fighting with strangers | 55 (18.0) | 4.47 (2.89 to 6.89)*** | 3.00 (1.87 to 4.80)*** |
| Serious repetitive violence | 46 (37.7) | 11.59 (6.34 to 21.21)*** | 5.33 (2.77 to 10.27)*** |
| Violence typologies | ASPD, n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 74 (1.0) | Reference | Reference |
| General violence | 40 (14.3) | 7.18 (4.18 to 12.34)*** | 4.55 (2.54 to 8.14)*** |
| Intimate/family violence | 13 (10.5) | 6.51 (2.85 to 14.88)*** | 4.70 (1.78 to12.38)** |
Among men, the ‘no violence’ class had the lowest prevalence of ASPD at 3.4%, followed by ‘minor violence’ at 7.6%. In contrast, the ‘serious repetitive violence’ class had an ASPD prevalence of 37.7%. Using the ‘no violence’ class as a reference and after adjustment for demographics and depression, alcohol dependency, drug dependency, anxiety and psychosis, all other classes of violence were significantly associated with an increased risk for ASPD with the exception of ‘minor violence’ (see Table 34).
Female distribution of ASPD across the classes was 1.0% for the ‘no violence’ class, 14.3% for the ‘general violence’ class and 10.5% for the ‘intimate/family violence’ class. Multinomial logistic regression models to estimate associations between violence latent classes and ASPD showed significant associations, after adjustments, for both violent classes (see Table 35).
Discussion
The demographic characteristics of those in the general population of Great Britain who reported violence in the past 5 years were very similar to the demographic characteristics of those who received a diagnosis of ASPD. In both cases they were more likely to be male, younger, single or separated, of lower social class and with comorbid drug dependency, alcohol dependency and anxiety disorder. Of those with ASPD, 1 in 10 reported that they had been violent in the past 5 years, which corresponds to the somewhat higher prevalence of 14% reported in a meta-analysis by Yu et al. ,139 although this study contained a larger proportion of men and men in clinical and prison settings.
The only associations with violence outcomes, including measures of severity, victim types and locations of the violence, that were not significant were those with minor violence and violence towards friends, occurring in someone else’s home. This corresponds to the highly versatile quality of criminal offending among prisoners with ASPD and the extensive overlap with the criminological construct of the career criminal. 128,130 Previous studies of offending behaviour have shown associations with crimes involving financial gain, including burglary and theft, robbery and firearm offences, together with violent offences and drug offences. Criminal versatility is a component of psychopathy. 140 It is possible that individuals in the community with ASPD come into contact with multiple potential victims through their varied criminal activity. On the other hand, it could be argued that personality traits of deceitfulness, impulsivity, irritability and aggressiveness, reckless disregard for safety of self and others, consistent irresponsibility and lack of remorse mean that they are prone to violently victimise many individuals whom they come into contact with, in multiple locations, irrespective of whether or not they are engaged in criminal activities.
We found that co-occurring drug dependence, psychosis, alcohol dependence and anxiety all increased the odds of association with violence among those with ASPD. However, the difference between those with and those without this comorbid diagnosis was not statistically significant. This would suggest that co-occurring disorders can increase risk but that the risk of violence as a result of the personality disorder itself is already high, so that most of the violent behaviour associated with the diagnosis is explained by ASPD.
Antisocial personality disorder did not discriminate men involved in minor violence (class 2) from those who were not involved in violence. However, classes 3–5 were significantly associated with a diagnosis of ASPD, particularly class 5 (‘serious repetitive violence’), in which more than one-third of men received an ASPD diagnosis. This provides further support that class 5 contains individuals who exhibit early-onset persistent antisocial behaviour, corresponding to Moffitt’s141 classification.
Although it might be expected that class 2 among women (‘general violence’) would have a stronger association with ASPD, it was of some interest that 1 in 10 women involved in IPV as an exclusive category of violent behaviour received a diagnosis of ASPD. However, whether this represents a subgroup of women who have a violent disposition resulting in them becoming particularly aggressive towards their partners and members of their family or whether these women form partnerships with aggressive and antisocial men with whom they come into frequent conflict with cannot be determined by this study.
Study 2: borderline personality disorder
Objectives
The aims of the study were to investigate:
-
the prevalence of self-reported violence associated with BPD in a combined data set representative of the household population of Great Britain
-
the association of BPD with different characteristics of violence, victim types and locations after adjusting for sociodemographic factors and comorbid psychiatric morbidity
-
the impact of comorbid psychiatric disorder on the association of BPD with violence
-
whether or not potential associations between BPD and violence in the household population are sex dependent.
Methods
Sample
For the purpose of this analysis, we combined two data sets, the NHPMS 2000 and the APMS 2007, to provide a total of 15,734 subjects.
Borderline personality disorder was identified using the SCID-II screen. 53 Participants gave ‘yes’ or ‘no’ responses to specific questions derived from the diagnostic criteria administered on a laptop computer. The BPD category of DSM-IV Axis II138 was created by manipulating cut-off points to increase levels of agreement, measured by the kappa coefficient, between both individual criteria and clinical diagnoses.
Statistical analyses
Weighted (n) frequencies and proportions were reported on all categorical variables. Group associations between BPD and violence were established using binary logistic regression with the OR as the measure of magnitude. Multiple categorical predictor covariates were assigned a reference category against which other categories were contrasted.
We quantified the associations between positive BPD classification and the violence typologies described in Chapter 2 (see Study 2).
All statistical analyses were adjusted by including covariates in each model simultaneously. Adjustments were made for demographic factors, drug dependence, alcohol dependence, positive psychosis screening, ASPD and anxiety disorders. To adjust for the effects of selecting one individual per household and under-representation of certain subgroups, and to account for any deviation from selecting a simple random sample, all variance estimates were weighted. Details of the procedures used in weighting have been described previously. 45,82 All models employed robust SEs to adjust for clustering of individuals within postcodes. All analyses were performed using Stata.
Results
Demographic characteristics
Of 15,734 respondents, 1596 (10.1%) reported any violence in the past 5 years. The overall prevalence of BPD in the sample was 1.4%. Table 36 shows that the older age groups (35–54 years and ≥ 55 years) had significantly lower rates of BPD. Sex was not associated with BPD-positive screening. However, in adjusted models, being separated or divorced and being in the lowest social classes were all significantly associated with BPD. Anxiety disorders, drug and alcohol dependence, psychosis and ASPD were also all significantly associated with BPD in adjusted models.
| Covariate | Any violence in the past 5 years (n = 1596; 10.1%) | BPD (n = 220; 1.5%) | ||
|---|---|---|---|---|
| n (%) | AORa (95% CI) | n (%) | AORa (95% CI) | |
| Sex | ||||
| Female | 406 (5.1) | Reference | 124 (1.6) | Reference |
| Male | 1190 (15.4) | 3.41 (2.89 to 4.04)*** | 96 (1.3) | 0.66 (0.46 to 1.02) |
| Age groups (years) | ||||
| 16–34 | 1175 (22.4) | Reference | 138 (2.8) | Reference |
| 35–54 | 371 (6.3) | 0.33 (0.28 to 0.39)*** | 70 (1.3) | 0.56 (0.38 to 0.83)** |
| ≥ 55 | 51 (1.1) | 0.06 (0.05 to 0.09)*** | 12 (0.3) | 0.21 (0.11 to 0.38)*** |
| Marital status | ||||
| Married/cohabiting | 480 (4.7) | Reference | 73 (0.8) | Reference |
| Single | 973 (23.4) | 1.79 (1.50 to 2.14)*** | 111 (2.8) | 1.27 (0.81 to 2.00) |
| Separated/divorced | 144 (9.9) | 2.05 (1.63 to 2.58)*** | 35 (2.5) | 1.71 (1.08 to 2.70)* |
| Social class | ||||
| I and II | 302 (5.6) | Reference | 48 (0.9) | Reference |
| IIIM and IIINM | 708 (11.3) | 2.11 (1.75 to 2.54)*** | 87 (1.5) | 1.35 (0.90 to 2.02) |
| IV and V | 398 (12.6) | 2.42 (1.95 to 3.01)*** | 63 (2.1) | 1.71 (1.10 to 2.66)* |
| Ethnicity | ||||
| White | 1463 (10.2) | Reference | 201 (1.5) | Reference |
| Black | 49 (12.1) | 0.92 (0.58 to 1.46) | 7 (2.0) | 1.21 (0.47 to 3.10) |
| Indian subcontinent | 36 (7.0) | 0.46 (0.26 to 0.81)** | 7 (1.5) | 1.16 (0.27 to 4.90) |
| Other | 40 (11.2) | 0.90 (0.53 to 1.51) | 4 (1.4) | 0.32 (0.08 to 1.38) |
| Drug dependency | 276 (48.4) | 2.31 (1.74 to 3.08)*** | 53 (9.8) | 2.74 (1.59 to 4.70)*** |
| Alcohol dependency | 371 (35.3) | 2.21 (1.77 to 2.77)*** | 73 (7.2) | 2.84 (1.81 to 4.44)*** |
| Anxiety disorder | 375 (16.2) | 1.87 (1.55 to 2.24)*** | 158 (7.3) | 10.76 (7.31 to 15.83)*** |
| Psychosis | 7 (14.0) | 0.53 (0.16 to 1.79) | 12 (28.7) | 7.20 (2.80 to 18.49)*** |
| ASPD | 246 (46.2) | 2.84 (2.09 to 3.86)*** | 57 (11.3) | 2.92 (1.73 to 4.91)*** |
Main associations of borderline personality disorder with violence
Table 37 shows unadjusted and adjusted associations of BPD with all violent outcomes, victim types and locations of reported violence. Following adjustments, BPD was significantly associated with only IPV and violence in the respondent’s own home. Furthermore, logistic regression analyses stratified by sex revealed no associations between BPD and violence for women or men when carried out separately (p > 0.05).
| Outcomes | Violent total, n (%) | Violent within BPD group, n (%) | BPD, OR (95% CI) | BPD, AORa (95% CI) |
|---|---|---|---|---|
| Any violence | 1596 (10.1) | 80 (36.5) | 5.39 (3.87 to 7.49)*** | 1.50 (0.94 to 2.41) |
| Violence while intoxicated | 685 (4.3) | 52 (23.9) | 7.38 (5.01 to10.88)*** | 1.62 (0.90 to 2.91) |
| Severity of violence | ||||
| Minor violence | 655 (4.2) | 17 (7.6) | 1.96 (1.07 to 3.59)* | 0.92 (0.46 to 1.84) |
| Five or more violent incidents | 335 (2.1) | 25 (11.3) | 6.48 (3.92 to10.70)*** | 1.65 (0.83 to 3.29) |
| Victim injured | 505 (3.2) | 26 (11.7) | 4.13 (2.59 to 6.60)*** | 0.72 (0.38 to 1.37) |
| Perpetrator injured | 514 (3.3) | 37 (17.0) | 6.34 (4.10 to 9.80)*** | 1.25 (0.66 to 2.38) |
| Police involved | 431 (2.7) | 32 (14.5) | 6.60 (4.21 to 10.36)*** | 1.44 (0.76 to 2.73) |
| Victim of violence | ||||
| Intimate partner | 252 (1.6) | 32 (14.7) | 11.79 (7.53 to 18.46)*** | 1.92 (1.04 to 3.54)* |
| Family member | 153 (1.0) | 8 (3.7) | 3.87 (1.84 to 8.17)*** | 1.34 (0.52 to 3.43) |
| Friend | 312 (2.0) | 22 (11.1) | 5.96 (3.45 to10.30)*** | 1.46 (0.64 to 3.32) |
| Person known | 511 (3.2) | 27 (12.3) | 4.39 (2.68 to 7.20)*** | 1.00 (0.51 to 1.97) |
| Stranger | 783 (5.0) | 34 (15.7) | 3.76 (2.38 to 5.92)*** | 0.99 (0.53 to 1.83) |
| Police | 88 (0.6) | 7 (3.0) | 6.12 (2.45 to15.31)*** | 0.97 (0.22 to 4.22) |
| Other | 108 (0.7) | 5 (2.2) | 3.49 (1.05 to11.59)* | 0.46 (0.09 to 2.36) |
| Location of violent incident | ||||
| Own home | 292 (1.9) | 32 (14.7) | 9.98 (6.35 to15.67)*** | 2.17 (1.19 to 3.95)* |
| Someone else’s home | 138 (0.9) | 9 (4.2) | 5.58 (2.53 to12.32)*** | 0.95 (0.31 to 2.89) |
| Street | 909 (5.8) | 53 (24.0) | 5.52 (3.84 to 7.93)*** | 1.34 (0.77 to 2.33) |
| Bar/pub | 541 (3.4) | 37 (16.8) | 6.13 (3.94 to 9.52)*** | 1.51 (0.80 to 2.85) |
| Workplace | 101 (0.6) | 4 (1.8) | 3.01 (1.00 to 9.06)* | 1.22 (0.37 to 4.01) |
| Other | 241 (1.5) | 11 (5.0) | 3.54 (1.60 to 7.81)** | 0.95 (0.33 to 2.77) |
Impact of coexisting disorders on the association of borderline personality disorder with any violence
We performed logistic regression analyses stratified by comorbid psychiatric morbidity (presence/absence) to examine the impact of specific co-occurring disorders on the association of BPD with any reported violence. Table 38 shows the results of these adjusted analyses. BPD was not significantly associated with reports of any violence in the absence of any comorbid disorders, except for ASPD. Associations with violence were present when BPD was specifically comorbid with drug dependency, alcohol dependency and anxiety disorders (see Table 38). Despite significant associations in the presence of these comorbid disorders, the increased levels that we observed were not statistically significant.
| Comorbid category | Without comorbidity, AORa (95% CI) | With comorbidity |
|---|---|---|
| Drug dependence | 1.39 (0.80 to 2.42) | 2.58 (1.17 to 5.70)* |
| Psychosis | 1.49 (0.92 to 2.40) | 0.77 (0.27 to 2.22) |
| Alcohol | 1.19 (0.67 to 2.12) | 2.93 (1.33 to 6.46)** |
| Anxiety | 1.26 (0.54 to 2.94) | 2.42 (1.38 to 4.24)** |
| ASPD | 1.84 (1.13 to 3.01)* | 1.41 (0.67 to 2.98) |
Violence classes and borderline personality disorder
Latent class models derived five violence classes for men and three violence classes for women in this joint data set. (see Chapter 2, Study 2.) Tables 39 and 40 report associations between individuals with BPD and the violence classes for men and women respectively.
| Violence typologies | BPD, n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 45 (0.7) | Reference | Reference |
| Minor violence | 9 (2.0) | 2.06 (0.76 to 5.55) | 1.31 (0.46 to 3.72) |
| Violence towards known persons | 22 (7.7) | 6.22 (2.72 to 14.23)*** | 2.32 (0.92 to 5.86) |
| Fighting with strangers | 9 (3.1) | 3.32 (1.18 to 9.32) | 1.22 (0.41 to 3.69) |
| Serious repetitive violence | 11 (9.9) | 8.81 (3.40 to 22.80)*** | 2.02 (0.67 to 6.10) |
| Violence typologies | BPD, n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 94 (1.3) | Reference | Reference |
| General violence | 22 (8.2) | 3.30 (1.80 to 6.03)*** | 1.40 (0.66 to 2.97) |
| Intimate/family violence | 7 (5.8) | 3.09 (1.42 to 6.72)** | 1.17 (0.44 to 3.11) |
For men, the ‘no violence’ class 1 had the lowest prevalence of BPD at 0.7%, followed by ‘minor violence’ at 2.0%. The ‘serious repetitive violence’ class had a BPD prevalence of 9.9%. Using the ‘no violence’ class as a reference, none of the remaining classes was significantly associated with BPD in the fully adjusted model (see Table 39).
Female distribution of BPD across the classes was 1.3% for the ‘no violence’ class, 8.2% for the ‘mild/moderate violence’ class and 5.8% for the ‘intimate/family violence’ class. Multinomial logistic regression models to estimate associations between violence latent classes and BPD showed no significant associations after full adjustment (see Table 39).
Discussion
Borderline personality disorder is the category of personality disorder that is most commonly found among patients in mental health services. There has been more research carried out into BPD, particularly into its treatment, than into any other Axis II disorder. In the past, the condition was considered to be associated with difficulties in management, particularly on an inpatient basis, and in this chapter we have described how personality disorders, including BPD, were excluded from many services. There is no information to indicate whether improvements in treatment have resulted in mental health professionals becoming more comfortable with the treatment of patients with BPD. However, more are now treated in the community with psychological therapies than in the past and avoiding hospitalisation is considered an important goal. Although there are several features of BPD that might be expected to result in violence, and thereby cause difficulties in treatment and management, our study did not show an increased risk of violence among members of the general household population with a diagnosis of BPD.
We found in this large representative sample that there was no difference in the prevalence of BPD between men and women. However, as with violence in the population, the diagnosis was uncommon among older people. It was more common among those who were separated and those of lower social classes. BPD was highly comorbid with other conditions, including psychosis and anxiety disorder, as well as with substance dependence and ASPD. Our study revealed that, when those with BPD do behave violently, it is most likely to be driven by comorbid conditions, including drug and alcohol dependency and anxiety disorder, rather than by the personality disorder. Because BPD was not a factor in determining the general level of violence in the household population, it did not discriminate between the classes of violence for either men or women. The only association that we found between violence and BPD was for IPV and violence occurring in the person’s own home. This corresponds to the demographic finding that this condition was more common among those who were separated and to criterion 2 of the DSM-IV BPD diagnosis: ‘a pattern of unstable and intense interpersonal relationships characterised by alternating between extremes of idealisation and devaluation’. 138 It may also correspond to criterion 1, ‘frantic efforts to avoid real or imagined abandonment’, resulting in altercations with partners who threaten to leave.
Chapter 5 Neurodevelopmental disorder and violence
Part of this chapter was first published in González R, Kallis C, Coid JW. Attention deficit hyperactivity disorder and violence in the population of England: does comorbidity matter? PLOS ONE 2013;8:e75575. It is reused here under the terms of the Creative Commons Attribution (CC BY) licence.
Background
Reports describing high rates of neuropsychiatric abnormalities among death row inmates, forensic psychiatric inpatients and others with a history of violence have led in the past to assertions that evidence of brain-behavioural impairment may mitigate or excuse criminal behaviour. 142–145 It has been argued that there is an episodic dyscontrol syndrome related to minimal brain dysfunction and complex partial seizures146 and that violently recidivistic criminals are men who are likely to have abnormal brain biology. 147 Neuroimaging studies suggest that the brain areas associated with violent and impulsive acts are located in the prefrontal cortex and medial temporal regions. 148 However, although a higher level of brain abnormalities tends to be found in individuals who are violent, particularly those who are repetitively violent, there is no established cause of association between brain pathology and violent behaviour.
An alternative approach is to consider violence as one form of antisocial behaviour that may be understood as a disorder having neurodevelopmental origins that, alongside autism, hyperactivity and dyslexia, shows strong male preponderance, early childhood onset, subsequent persistence and low prevalence in the population. These features were described by Moffitt142 in early-onset, persistent antisocial behaviour. Moffitt85 later argued that one form of antisocial behaviour, the early-onset persistent type, is a neurodevelopmental disorder. The other form of antisocial behaviour, afflicting females as well as males, is common and emerges in the context of social relationships.
Chapter 4 examined the strong association between violence and individuals with ASPD, who fulfil many of the criteria of Moffitt’s141 early-onset persistent subtype (see Study 1). In this chapter we examine associations between violence and childhood neurodevelopmental disorders and proxy measures of neurodevelopmental disorders at the population level among adults. When investigating intelligence, it could be rightly argued that this is not an accurate measure of neuropsychological development as much as educational and social functioning and at a highly complex level. However, taking a simplistic view, a high score for intelligence can be considered to reflect a high level of functioning and might therefore be presumed to have a negative association with neuropsychological deficits that might lead to violent behaviour at the population level.
Childhood neurodevelopmental disorders have been studied in relation to violent criminality using population-based registers of child and adolescent mental health services in Stockholm, Sweden. 149 No association with violent crime could be observed for autistic spectrum disorders (ASDs), although ADHD showed an elevated risk of committing a violent crime.
Study 1: intelligence and violence
Objectives
The objectives of this study were to investigate:
-
the association between total intelligence quotient (IQ) score and violent behaviour in the general population to identify the risk or protective effects of a verbal measure of IQ
-
potential direct associations between total IQ score and violent behaviour in the general population by adjusting for demographic factors that are linked to both IQ and violence perpetration
-
whether or not there are mean differences in IQ score according to a violence typology for men and women.
Methods
Sample
For the purpose of this study we combined two data sets, the NHPMS 2000 and the APMS 2007, to provide a sample of 15,734 men and women.
Intellectual functioning
Intellectual functioning was estimated using the National Adult Reading Test (NART),150 a proxy measure of pre-morbid IQ that includes 50 words printed in order of increasing difficulty. Originally developed to predict Wechsler Adult Intelligence Scale (WAIS) IQ scores,151 the NART was subsequently restandardised to predict WAIS-Revised IQ scores. Acceptable construct validity152 and high correlations with measures of IQ have been consistently reported for the NART. 153,154
Statistical analyses
As each of the surveys employed the same measures of IQ, demography and violence outcomes, we conducted joint analyses of individual-level data.
Weighted frequencies and proportions were reported for categorical predictors and outcomes. We estimated their impact on violence using logistic regression models, with ORs as indicators of the magnitude of associations.
All models are presented (1) unadjusted and (2) adjusted for social class, sex, age, marital status and ethnicity. Demographic variables significantly associated with either exposure (IQ) or outcome (violence) were included as confounders in multivariable models.
The IQ total mean score differences between the latent violence classes were estimated through analysis of variance (ANOVA) with Bonferroni post hoc tests.
To adjust for the effects of selecting one individual per household and under-representation of certain subgroups, and to account for any deviation from selecting a simple random sample, probability weights were used. All models employed robust SEs to adjust for clustering of individuals within postcodes. To control for differences between the two sources of data, survey was included as a fixed factor on model estimates.
Results
Demographic characteristics
All age categories above 16–34 years were protective for ‘any violence’. Participants from the Indian subcontinent were also less likely to report perpetration of violence. Being male, single or separated or divorced and from social classes lower than I and II singificantly increased the likelihood of violence (Table 41).
| Covariate | Any violence in the past 5 years (n = 1596; 10.1%) | IQ total score | ||
|---|---|---|---|---|
| n (%) reported | AORa (95% CI) | Mean (SD) | βb | |
| Sex | ||||
| Male | 1190 (15.4) | 3.78 (3.23 to 4.43)*** | 103.0 (16.6) | –0.45 |
| Female | 406 (5.1) | Reference | 103.1 (15.4) | Reference |
| Age group (years) | ||||
| 16–34 | 1175 (22.4) | Reference | 98.0 (15.0) | Reference |
| 35–54 | 371 (6.3) | 0.31 (0.26 to 0.37)*** | 105.0 (15.3) | 7.36*** |
| 55–74 | 49 (1.2) | 0.06 (0.04 to 0.08)*** | 104.6 (16.3) | 7.08*** |
| ≥ 75 | 2 (0.3) | 0.02 (0.01 to 0.07)*** | 103.5 (17.1) | 5.99*** |
| Marital status | ||||
| Married | 480 (4.7) | Reference | 104.4 (15.6) | Reference |
| Single | 973 (23.4) | 2.05 (1.73 to 2.43)*** | 99.7 (16.3) | –6.13*** |
| Separated/divorced | 144 (9.9) | 2.47 (1.99 to 3.08)*** | 102.5 (15.8) | –2.11*** |
| Social class | ||||
| I and II | 302 (5.6) | Reference | 110.7 (13.5) | Reference |
| IIIM and IIINM | 708 (11.3) | 2.20 (1.84 to 2.62)*** | 101.3 (15.1) | –9.86*** |
| IV and V | 398 (12.6) | 2.58 (2.09 to 3.18)*** | 95.0 (15.3) | –15.69*** |
| Ethnicity | ||||
| White | 1463 (10.2) | Reference | 103.3 (15.8) | Reference |
| Black | 49 (12.1) | 0.83 (0.53 to 1.31) | 92.9 (16.9) | –8.68*** |
| Indian subcontinent | 36 (7.0) | 0.38 (0.21 to 0.66)** | 96.0 (16.1) | –6.48*** |
| Other | 40 (11.2) | 0.94 (0.57 to 1.54) | 100.1 (15.8) | –3.49** |
Table 41 also shows differences in IQ score according to each sociodemographic variable. There were no differences in IQ by sex. Age was positively associated with IQ score. Being single or separated/divorced, from social classes lower than I and II and from any ethnic group other than white were all associated with a lower IQ score.
Main associations of intellectual functioning with violence outcomes
Total IQ score showed significant protective associations with all violent outcomes. The higher the IQ score, the less likely the individual was to report violence (Table 42). Most of these findings remained significant after adjusting for all sociodemographic characteristics, with certain exceptions: associations with minor violence, violence towards family members or strangers and violence in the home or the workplace were no longer significantly associated with IQ score after adjustment.
| Outcomes | n (%) violent | OR (95% CI) | AORa (95% CI) |
|---|---|---|---|
| Any violence | 1596 (10.1) | 0.97 (0.97 to 0.98)*** | 0.99 (0.98 to 0.99)*** |
| Violence while intoxicated | 685 (4.3) | 0.97 (0.97 to 0.98)*** | 0.99 (0.98 to 1.00)** |
| Severity of violence | |||
| Minor violence | 655 (4.2) | 0.98 (0.97 to 0.98)*** | 0.99 (0.99 to 1.00) |
| Five or more violent incidents | 335 (2.1) | 0.97 (0.96 to 0.97)*** | 0.99 (0.98 to 1.00)* |
| Victim injured | 505 (3.2) | 0.97 (0.96 to 0.98)*** | 0.99 (0.98 to 1.00)* |
| Perpetrator injured | 514 (3.3) | 0.97 (0.96 to 0.98)*** | 0.99 (0.98 to 1.00)* |
| Police involved | 431 (2.7) | 0.97 (0.96 to 0.98)*** | 0.98 (0.98 to 0.99)*** |
| Victim of violence | |||
| Intimate partner | 252 (1.6) | 0.97 (0.96 to 0.98)*** | 0.98 (0.97 to 0.99)*** |
| Family member | 153 (1.0) | 0.97 (0.96 to 0.99)*** | 0.99 (0.98 to 1.01) |
| Friend | 312 (2.0) | 0.96 (0.95 to 0.97)*** | 0.98 (0.97 to 0.99)** |
| Known person | 511 (3.2) | 0.96 (0.95 to 0.97)*** | 0.98 (0.97 to 0.99)*** |
| Stranger | 783 (5.0) | 0.98 (0.97 to 0.98)*** | 1.00 (0.99 to 1.00) |
| Police | 88 (1.0) | 0.95 (0.94 to 0.97)*** | 0.96 (0.95 to 0.98)*** |
| Location of violent incident | |||
| Own home | 292 (1.9) | 0.98 (0.97 to 0.99)*** | 0.99 (0.99 to 1.00) |
| Someone else’s home | 138 (0.9) | 0.97 (0.96 to 0.98)*** | 0.99 (0.98 to 1.01) |
| Bar/pub | 541 (3.4) | 0.97 (0.96 to 0.97)*** | 0.98 (0.97 to 0.99)*** |
| Street/outdoors | 909 (5.8) | 0.97 (0.97 to 0.98)*** | 0.99 (0.98 to 1.00)** |
| Workplace | 101 (0.6) | 0.99 (0.97 to 1.00)* | 1.00 (0.99 to 1.02) |
Violence classes and intelligence quotient
There was variation in the mean IQ score according to the violence typologies for men and women. In men, the mean IQ score was 99.9 (SD 25.9) for those in the ‘no violence’ class, 95.7 (SD 20.1) for those in the ‘minor violence’ class, 93.0 (SD 18.7) for those in the ‘violence towards known persons’ class, 97.5 (SD 20.9) for those in the ‘fighting with strangers’ class and 93.7 (SD 14.6) for those in the ‘serious repetitive violence’ class. For women, the mean IQ score was 100.0 (SD 24.0) for those in the ‘no violence’ class, 92.3 (SD 15.9) for those in the ‘general violence’ class and 100.2 (SD 18.7) for those in the ‘intimate/family violence class’. There were significant differences in mean IQ score between the classes for both men (F = 7.2, p < 0.001) and women (F = 13.1, p < 0.001). Post hoc tests revealed that men in the ‘violence towards known persons’ class had a significantly lower mean IQ score than men in the ‘no violence’ class (p < 0.01). Women in the ‘general violence’ class had a significantly lower mean IQ score than those in the ‘no violence’ and ‘intimate/family violence’ classes (p < 0.01).
Discussion
An extensive body of research has previously linked low IQ score and intellectual disability to delinquency, serious crime and interpersonal violence. 32,155–157 Our findings therefore correspond in showing a negative association with all violent outcomes except minor violence, violence towards family members and strangers and violence in the home or in the workplace. This would imply that a higher IQ score conveys a protective effect against violence, independent of demography. This corresponds to evidence on the protective effects of above-average intelligence and intact cognition on both general health158 and antisocial behaviours. 159
Only two classes of the violence typology were significantly associated with lower IQ score than in the general population. Nevertheless, these are of considerable interest in the context of our findings reported in other chapters. Class 4 in men (‘violence towards known persons’) was associated with impaired social functioning in that these individuals were unemployed and economically inactive. A proportion were also dependent on drugs and alcohol and more had shown difficulty in maintaining close relationships.
Class 2 (‘general violence’) in women showed the lowest mean IQ score of any subgroup, with the mean score also being lower than those of the male classes. This group showed some similarities to classes 4 and 5 (‘serious repetitive violence’) among men in that women appeared to be socially dysfunctional. Intellectual functioning was hence identified as an important distinguishing characteristic of this subgroup.
Study 2: attention deficit hyperactivity disorder and violence
Objectives
The objectives of this study were to:
-
estimate the prevalence of self-reported violence associated with ADHD and its severity and victims and the location of violent incidents
-
investigate the independent associations between the symptom dimensions of ADHD: inattention and hyperactivity/impulsivity
-
investigate the associations of ADHD with a typology of violence in the general population.
Methods
Sample
For the purpose of this analysis we used a single data set, the APMS 2007, which recruited adults aged ≥ 16 years living in private households in England.
Measures
The survey included the six-item version of the Adult Self-Report Scale (ASRS),160 based on DSM-IV diagnostic criteria as a measure of ADHD. The ASRS has strong concordance with clinical diagnoses, with a reported AUC of 0.90. Four items are related to inattention and two to hyperactivity. Items are based on a 5-point Likert scale. We used the ASRS-6 scoring and classification methods, with a binary cut-off point of ≥ 13. 161 To explore the relative contributions of ADHD symptoms, the sum of the response scores was calculated for the inattention items (items 1–4) and the hyperactivity items (items 5 and 6).
Statistical analyses
Weighted frequencies and proportions were reported for all categorical variables. Group associations were established using binary logistic regression with the OR as the measure of magnitude. Multiple categorical predictor covariates were assigned a reference category against which other categories were contrasted.
Two approaches examined the associations of ADHD with dependent binary measures of violence. In the first approach the exposure was a binary measure of ADHD, based on a cut-off point of ≥ 13. 160 In the second approach we estimated hyperactivity and inattention scores as an alternative to the ADHD categorical classifications using symptom dimension scores.
Additionally, we quantified the associations between ADHD classification and the violence typologies described in Chapter 2 (see Study 2).
All statistical analyses were adjusted by including covariates in each model simultaneously. Adjustments were made for demographic factors, ASPD, drug dependence, alcohol dependence and anxiety disorders. To adjust for the effects of selecting one individual per household and under-representation of certain subgroups, and to account for any deviation from selecting a simple random sample, all variance estimates were weighted. Details of the procedures used in weighting have been described previously. 45,82 All models employed robust SEs to adjust for clustering of individuals within postcodes. All analyses were performed using Stata.
Results
The overall prevalence of ADHD in the sample was 5.7%. The prevalence of any violence in the past 5 years was 8.4%. The older age group (≥ 55 years) had a significantly lower rate of ADHD. Being male increased the association with ADHD. Anxiety disorders, ASPD, psychotic symptoms and alcohol dependence were significantly associated with ADHD (Table 43).
| Covariate | Any violence in the past 5 years (n = 614; 8.4%) | ADHD (n = 424; 5.7%) | ||
|---|---|---|---|---|
| n (%) reported | AORa (95% CI) | n (%) reported | AORa (95% CI) | |
| Sex | ||||
| Female | 173 (4.6) | Reference | 305 (5.4) | Reference |
| Male | 441 (12.3) | 3.14 (2.42 to 4.07)*** | 219 (6.1) | 1.41 (1.09 to 1.81)** |
| Age group (years) | ||||
| 16–34 | 448 (19.9) | Reference | 189 (8.3) | Reference |
| 35–54 | 141 (5.4) | 0.32 (0.24 to 0.42)*** | 171 (6.5) | 1.01 (0.74 to 1.38) |
| ≥ 55 | 25 (1.0) | 0.06 (0.04 to 0.10)*** | 64 (2.6) | 0.47 (0.32 to 0.68)*** |
| Marital status | ||||
| Married/cohabiting | 235 (4.6) | Reference | 225 (4.4) | Reference |
| Single | 340 (20.4) | 1.61 (1.20 to 2.16)** | 157 (9.4) | 1.10 (0.79 to 1.53) |
| Divorced/separated | 39 (7.2) | 1.70 (1.14 to 2.54)** | 42 (7.6) | 1.16 (0.82 to 1.64) |
| Social class | ||||
| I and II | 114 (4.3) | Reference | 110 (4.2) | Reference |
| IIIM and IIINM | 264 (9.5) | 2.10 (1.55 to 2.84)*** | 158 (5.6) | 1.27 (0.93 to 1.73) |
| IV and V | 149 (10.5) | 2.15 (1.51 to 3.08)*** | 101 (7.1) | 1.40 (0.99 to 1.98) |
| Ethnicity | ||||
| White | 550 (8.3) | Reference | 366 (5.5) | Reference |
| Black | 21 (9.3) | 0.87 (0.43 to 1.76) | 22 (9.9) | 1.67 (0.89 to 3.14) |
| Indian subcontinent | 19 (6.9) | 0.50 (0.23 to 1.08) | 10 (3.7) | 0.66 (0.29 to 1.50) |
| Other | 19 (8.7) | 0.78 (0.37 to 1.63) | 20 (9.4) | 1.35 (0.66 to 2.76) |
| ASPD | 80 (41.1) | 2.26 (1.40 to 3.64)** | 45 (22.6) | 1.72 (1.02 to 2.91)* |
| Drug dependency | 111 (44.2) | 3.36 (2.18 to 5.16)*** | 46 (18.1) | 1.64 (0.93 to 2.91) |
| Alcohol dependency | 106 (24.5) | 1.38 (0.95 to 2.00) | 74 (17.0) | 1.70 (1.09 to 2.65)* |
| Anxiety disorder | 165 (15.2) | 2.07 (1.54 to 2.80)*** | 228 (20.8) | 6.89 (5.37 to 8.85)*** |
| Psychosis | 23 (24.5) | 1.77 (0.83 to 3.81) | 33 (33.6) | 3.13 (1.78 to 5.53)*** |
Main associations of attention deficit hyperactivity disorder with violence
Table 44 shows the numbers of respondents who reported the different violence outcomes. Following adjustment, an association with ADHD was observed only for having been involved in any violence in the past 5 years and in violent events considered minor.
| Outcomes | n (%) violent | ADHD, AORa (95% CI) | Hyperactivity, ORb (95% CI) | Inattention, ORb (95% CI) |
|---|---|---|---|---|
| Any violence | 614 (8.4) | 1.75 (1.14 to 2.68)* | 1.15 (1.08 to 1.23)*** | 1.06 (1.02 to 1.11)** |
| Violence while intoxicated | 263 (3.6) | 1.34 (0.72 to 2.48) | 1.15 (1.04 to 1.26)** | 1.03 (0.97 to 1.10) |
| Severity of violence | ||||
| Minor violence | 247 (3.3) | 2.54 (1.48 to 4.34)** | 1.18 (1.07 to 1.30)** | 1.11 (1.05 to 1.18)** |
| Five or more violent incidents | 98 (1.3) | 1.42 (0.49 to 4.15) | 1.16 (0.96 to 1.40) | 1.01 (0.92 to 1.11) |
| Victim injured | 172 (2.3) | 0.88 (0.37 to 2.14) | 1.10 (0.98 to 1.24) | 0.99 (0.92 to 1.06) |
| Perpetrator injured | 204 (2.8) | 1.10 (0.55 to 2.20) | 1.06 (0.95 to 1.18) | 1.00 (0.94 to 1.07) |
| Police involved | 177 (2.4) | 1.19 (0.56 to 2.55) | 1.15 (1.02 to 1.28)* | 0.97 (0.90 to 1.04) |
| Victim of violence | ||||
| Intimate partner | 115 (1.6) | 1.52 (0.70 to 3.28) | 1.16 (1.01 to 1.32)* | 1.05 (0.97 to 1.14) |
| Family member | 91 (1.2) | 1.77 (0.70 to 4.44) | 1.26 (1.08 to 1.46)** | 1.04 (0.94 to 1.16) |
| Friend | 132 (1.8) | 1.53 (0.60 to 3.89) | 1.18 (1.02 to 1.37)* | 1.06 (0.97 to 1.16) |
| Person known | 195 (2.6) | 1.16 (0.57 to 2.36) | 1.10 (0.98 to 1.23) | 1.03 (0.95 to 1.11) |
| Stranger | 300 (4.1) | 1.15 (0.64 to 2.08) | 1.07 (0.97 to 1.18) | 1.04 (0.99 to 1.10) |
| Location of violent incident | ||||
| Own home | 123 (1.7) | 1.75 (0.87 to 3.52) | 1.21 (1.06 to 1.39)** | 1.02 (0.94 to 1.11) |
| Someone else’s home | 61 (0.8) | 1.25 (0.49 to 3.17) | 1.21 (1.05 to 1.40)* | 0.93 (0.83 to 1.03) |
| Bar/pub | 183 (2.5) | 1.50 (0.74 to 3.05) | 1.17 (1.05 to 1.31)** | 1.01 (0.93 to 1.10) |
| Workplace | 21 (0.3) | 2.79 (0.83 to 9.39) | 1.25 (1.01 to 1.54)* | 1.14 (0.98 to 1.32) |
Attention deficit hyperactivity disorder symptom dimensions and violence: hyperactivity and inattention
To estimate the independent contributions of hyperactivity and inattention, we developed adjusted models replacing the ADHD binary variable with continuous scores of hyperactivity and inattention. The inattention and hyperactivity scores were entered in models simultaneously (to adjust for each other). Table 44 shows that inattention may be associated with any violence, but specifically only with minor violence. However, there were additional independent associations with hyperactivity, including any violence, violence while intoxicated and police involvement. Direct associations with hyperactivity were also observed for specific victim types (intimate partner, family members and friends) and locations of violence (in the participant’s or another person’s home, in a bar/pub or in the workplace). However, hyperactivity was not associated with more severe violence leading to injury to victim or perpetrator and was associated with minor violence.
Violence classes and attention deficit hyperactivity disorder
Latent class models derived five violence classes for men and three violence classes for women in the 2007 APMS. Development of these LCAs is described in detail in Chapter 2. Tables 45 and 46 include the associations between individuals with ADHD and the violence classes for men and women respectively.
| Violence typologies | ADHD, n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 150 (4.8) | Reference | Reference |
| Minor violence | 28 (16.1) | 3.51 (1.88 to 6.56)*** | 1.29 (0.46 to 3.63) |
| Violence towards known persons | 18 (16.3) | 2.10 (0.93 to 4.76) | 2.23 (0.88 to 5.62) |
| Fighting with strangers | 17 (14.2) | 2.96 (1.40 to 6.25)** | 1.23 (0.41 to 3.70) |
| Serious repetitive violence | 6 (19.1) | 2.74 (0.84 to 9.01) | 1.87 (0.62 to 5.67) |
| Violence typologies | ADHD, n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 181 (5.0) | Reference | Reference |
| General violence | 16 (12.6) | 1.67 (0.76 to 3.68) | 1.42 (0.68 to 2.99) |
| Intimate/family violence | 7 (17.0) | 3.50 (1.62 to 7.52)** | 1.14 (0.42 to 3.04) |
For men, the ‘no violence’ class had the lowest prevalence of ADHD at 4.8%; this was followed by ‘fighting with strangers’ at 14.2%, ‘minor violence’ at 16.1%, ‘violence towards known persons’ at 16.3% and ‘serious repetitive violence’ at 19.1%. With the ‘no violence’ class as a reference, none of the remaining classes was significantly associated with ADHD after adjustment for psychopathology (see Table 45).
The female distribution of ADHD across the classes was 5.0% for the ‘no violence’ class, 12.6% for the ‘general violence’ class and 17.0% for the ‘intimate/family violence’ class. Multinomial logistic regression models implemented to estimate associations between violence latent classes and ADHD revealed no significant associations after adjustment for psychopathology (see Table 46).
Discussion
In this study we aimed to examine the main effects of ADHD among individuals involved in violent incidents, as well as its impact on the severity, frequency, targets and locations of violence. After adjustments, ADHD appeared to be only moderately associated with violence, with the significant association observed being between ADHD and violent incidents classified as minor.
After examining two continuous measures using symptom dimensions, inattention demonstrated little or no impact on violence in the population and involved only minor incidents. On the other hand, hyperactivity was directly associated with violent incidents that resulted in police involvement, with reports of victims in close relationships and with violence taking place in the perpetrator’s or someone else’s home as well as in pubs and bars.
This finding is consistent with two other epidemiological studies that have reported significant associations between retrospective measures of ADHD and violent outcomes. 162,163 Our findings also provide validation of what has been evidenced by small study samples in forensic settings, which indicate an association between ADHD symptoms and violent breaches of discipline and offending. 164,165 However, in the general population, our findings suggest that direct associations of ADHD are restricted to violence towards those in intimate relationships and relatives. ADHD did not discriminate between the violence classes that we identified in Chapter 2 (see Study 2).
This study confirmed the importance of the association of hyperactivity with behavioural disturbance and impulsivity. Hyperactivity has been linked to an increased risk of aggression among adults. 166 This corresponds to the view that aggression and violence stem from deficient self-regulatory processes, including response disinhibition, failure to delay gratification, and emotional reactivity and dysregulation. 167–169
Study 3: autism and violence
Objective
The objective of this study was to investigate the prevalence of self-reported violence associated with autism and associated spectrum disorders in the household population.
Methods
Sample
The sample was drawn from the APMS 2007, a national survey of psychiatric morbidity among adults aged ≥ 16 years living in households in England in 2007. A total of 7403 participants completed the survey (response rate 57.0%). The design and sampling procedures have previously been described. 45,82
Autism was diagnosed in this sample using the Autism Diagnostic Observation Schedule module-4 (ADOS-4), which provides a face-to-face assessment of current respondent behaviours for an autistic disorder. 170 Non-specific pervasive developmental disorder (PDD) and autism classifications can be derived from ADOS-4 algorithm scores of ≥ 7 and ≥ 10 respectively. 171
Statistical analyses
Weighted frequencies and proportions were reported for all categorical variables. Group associations between autism and violence were established using binary logistic regression with the OR as the measure of magnitude. Multiple categorical predictor covariates were assigned a reference category against which other categories were contrasted.
All statistical analyses were adjusted by including covariates in each model simultaneously. Adjustments were made for demographic factors, alcohol dependence, psychosis, drug dependence, ASPD, depression and anxiety disorders. To adjust for the effects of selecting one individual per household and under-representation of certain subgroups, and to account for any deviation from selecting a simple random sample, all estimates were weighted. Details of the procedures used in weighting have been described previously. 82 All models employed robust SEs to adjust for clustering of individuals within postcodes. All analyses were performed using Stata.
Results
Demographic characteristics
Of 7361 respondents (weighted), 614 (8.4%) reported any violence in the past 5 years. Table 47 shows that the sociodemographic factors of male sex, marital status other than married and social classes lower than I and II were significantly associated with violence risk. Any age category above 34 years was protective.
| Covariate | Any violence in the past 5 years (n = 614; 8.4%) | Autism (n = 21; 0.3%) | ||
|---|---|---|---|---|
| n (%) reported | AORa (95% CI) | n (%) reported | AORa (95% CI) | |
| Sex | ||||
| Female | 173 (4.6) | Reference | 4 (0.1) | Reference |
| Male | 441 (12.3) | 3.17 (2.45 to 4.11)*** | 17 (0.5) | 4.12 (1.14 to 14.85)* |
| Age groups (years) | ||||
| 16–34 | 448 (19.9) | Reference | 11 (0.5) | Reference |
| 35–54 | 141 (5.4) | 0.32 (0.24 to 0.42)*** | 2 (0.1) | 0.22 (0.04 to 1.20) |
| ≥ 55 | 25 (1.0) | 0.06 (0.04 to 0.10)*** | 7 (0.3) | 0.80 (0.16 to 4.03) |
| Marital status | ||||
| Married/cohabiting | 235 (4.6) | Reference | 10 (0.2) | Reference |
| Single | 340 (20.4) | 1.60 (1.19 to 2.15)** | 8 (0.5) | 0.71 (0.11 to 4.52) |
| Divorced/separated | 40 (7.2) | 1.73 (1.16 to 2.58)** | 2 (0.4) | 2.11 (0.70 to 6.40) |
| Social class | ||||
| I and II | 114 (4.3) | Reference | 3 (0.1) | Reference |
| IIIM and IIINM | 264 (9.5) | 2.08 (1.54 to 2.82)*** | 8 (0.3) | 2.24 (0.61 to 8.27) |
| IV and V | 149 (10.5) | 2.15 (1.51 to 3.07)*** | 5 (0.4) | 3.34 (0.75 to 14.81) |
| Ethnicity | ||||
| White | 550 (8.3) | Reference | 21 (0.3) | Reference |
| Black | 21 (9.3) | 0.89 (0.44 to 1.82) | 0 (0.0) | No observations |
| Indian subcontinent | 19 (6.9) | 0.50 (0.23 to 1.07) | 0 (0.0) | No observations |
| Other | 19 (8.7) | 0.76 (0.36 to 1.61) | 0 (0.0) | No observations |
| Alcohol dependence | 106 (24.5) | 1.38 (0.94 to 2.01) | 1 (0.3) | 0.66 (0.08 to 5.16) |
| Psychosis | 3 (10.0) | 0.73 (0.10 to 5.35) | 0 (0.0) | No observations |
| Drug dependence | 111 (44.2) | 3.30 (2.14 to 5.10)*** | 0 (0.0) | No observations |
| Anxiety disorder | 165 (15.2) | 2.07 (1.53 to 2.80)*** | 4 (0.4) | 1.52 (0.55 to 4.23) |
| ASPD | 80 (41.1) | 2.30 (1.42 to 3.72)** | 4 (2.2) | 2.85 (0.39 to 20.68) |
| Depressive episode | 35 (16.3) | 1.23 (0.74 to 2.06) | 2 (1.0) | 3.95 (0.70 to 22.14) |
The overall weighted prevalence of autism in the sample in phase 1 of the two-phase survey was 0.3%. Being male was the only sociodemographic factor associated with autism.
Main associations of drug dependence with violence
There were no observations with categorically defined diagnosis of autism that reported any violence in the past 5 years associated with drug dependence. Using the less strict cut-off score for PDD of ≥ 7 revealed only two participants who were violent respondents.
Discussion
The prevalence of autism in the sample was very low, resulting in a problem of statistical power when searching for associations with violence. However, because there have been a series of media stories suggesting that there might be a link between violence and individuals with ASD (particularly Asperger’s syndrome) in cases of high-school shootings in the USA, it was important to observe that no cases of ASD were found. Only two cases were found in the population sample when the cut-off score was lowered to allow detection of more cases. This would suggest that there is little evidence for an association between ASD at the population level and violence. This is consistent with previous findings. 149
It has been observed that as the prevalence of autism spectrum disorders has increased, attention has shifted towards consideration of these disorders in adolescence and adulthood, as well as the public health repercussions for this population. The social and emotional deficits within these disorders could be relevant to certain cognitive features of criminal and violent behaviour that is not intended by the violent individual, for example impulsive behaviour triggered by stressful environments. Because of media attention over ASD and serious criminal acts, not always with clear evidence that the perpetrator actually has ASD, judicial and legislative state systems in the USA have begun to develop policies that lack an evidence base. 172 This has fortunately not occurred in the UK. Overall, there are thought to be three deficits characteristic of individuals with ASDs that might have a bearing on violent and criminal behaviour. These include theory of mind, emotional regulation and moral reasoning. However, there is no clear evidence base for these at present. More importantly, a key component of ASD is poor social interaction and social withdrawal. Because violence can be considered a highly ‘social’ activity, or at least it occurs most commonly in social settings, it should not be surprising that our study has failed to demonstrate an association between autism and violence.
Chapter 6 Substance dependence and violence
Background
Substance use is among the most consistently reported risk factor for violent behaviour. 173,174 Substance misuse is prevalent both among those in the general population reporting violence175 and among offenders convicted for violence. 176,177 In the NHPMS 2000, the highest percentage of violent incidents and the highest population-attributable risk were explained by individuals engaging in hazardous drinking followed by drug misuse. 83 Clinical studies of alcohol use have confirmed strong, if complex, associations with violence. 178 Furthermore, the high annual medical and social costs of injury from deliberate harm are highlighted by measures taken in A&E departments in the UK. These correlate with unemployment, poverty and, in particular, expenditure on alcohol. 179,180
Associations with substance misuse are highly complex. For example, Arseneault et al. 181 have argued that, because of involvement in the illegal economy of drugs markets, young people who are dependent on drugs must rely on violence to solve problematic transactions with dealers and others involved in drug-related social interactions. However, the high level of violence reported by drug-misusing individuals suggests that additional mechanisms must be operating in this association. 83 Furthermore, many drug misusers are also alcohol misusers and individuals with a propensity to violence may be inclined to abuse alcohol and drugs. The chemical effects of drugs and alcohol, including disinhibition, are thought by many to explain the association with violence, and many violent people attempt to explain their violence in terms of these chemical effects.
Because the associations are complex, we restricted our two studies of substance misuse reported in this chapter to the most severe level of dependence on drugs and alcohol.
Study 1: drug dependence
Objectives
The objectives of this study were to investigate:
-
the prevalence of self-reported violence associated with drug dependence in the household population of Great Britain
-
the associations between the characteristics of violence, victim types and locations of violence, after adjusting for sociodemographic factors, and drug dependence
-
the distribution of drug dependence and associations with a typology of violence in the general population.
Methods
Sample
For the purposes of this analysis we combined two data sets, the APMS 2007 and the NHPMS 2000, to provide a sample of 15,734 participants.
Definitions and assessment
Questions about drug use were asked in a computer-assisted self-completion interview. This included lifetime experience of 13 different types of illicit drugs together with patterns of use over the last year. Five questions derived from the Diagnostic Interview Schedule (DIS)182 were used to measure substance dependence.
Statistical analyses
Weighted frequencies and proportions were reported for all categorical variables. Group associations between drug dependence and violence were established using binary logistic regression with the OR as the measure of magnitude. Multiple categorical predictor covariates were assigned a reference category against which other categories were contrasted. The violence typologies for men and women described in Chapter 2 were regressed onto drug dependence using multinomial logistic regression.
All statistical analyses were adjusted by including covariates in each model simultaneously. Adjustments were made for demographic factors, psychosis, alcohol dependence, ASPD, depression and anxiety disorders. To adjust for the effects of selecting one individual per household and under-representation of certain subgroups, and to account for any deviation from selecting a simple random sample, all estimates were weighted. Details of the procedures used in weighting have been described previously. 45,82 All models employed robust SEs to adjust for clustering of individuals within postcodes. All analyses were performed using Stata.
Results
Demographic characteristics
Of the 15,734 respondents, 1596 (10.1%) reported any violence in the past 5 years. Table 48 shows that male sex, marital status other than married and social class lower than I and II were significantly associated with violence risk, whereas any age category > 34 years and being of Indian/Asian ethnicity were protective. The overall weighted prevalence of drug dependence in the sample was 3.6%. Male sex, marital status other than married and social class lower than I and II increased risk associations with drug dependence. Having an anxiety disorder or ASPD or alcohol dependence were directly associated with drug dependence in adjusted models.
| Covariate | Any violence in the past 5 years (n = 1596; 10.1%) | Drug dependence (n = 573; 3.6%) | ||
|---|---|---|---|---|
| n (%) | AORa (95 CI) | n (%) | AORa (95 CI) | |
| Sex | ||||
| Female | 406 (5.1) | Reference | 181 (2.3) | Reference |
| Male | 1190 (15.4) | 3.50 (2.95 to 4.14)*** | 392 (5.0) | 1.75 (1.35 to 2.27)*** |
| Age groups (years) | ||||
| 16–34 | 1175 (22.4) | Reference | 446 (8.5) | Reference |
| 35–54 | 371 (6.3) | 0.31 (0.26 to 0.37)*** | 98 (1.6) | 0.27 (0.19 to 0.36)*** |
| ≥ 55 | 51 (1.1) | 0.06 (0.04 to 0.08)*** | 29 (0.6) | 0.17 (0.11 to 0.26)*** |
| Marital status | ||||
| Married/cohabiting | 480 (4.7) | Reference | 132 (1.3) | Reference |
| Single | 973 (23.4) | 1.85 (1.55 to 2.20)*** | 389 (9.3) | 2.38 (1.78 to 3.18)*** |
| Divorced/separated | 144 (9.9) | 2.05 (1.63 to 2.58)*** | 52 (3.6) | 2.01 (1.38 to 2.93)*** |
| Social class | ||||
| I and II | 302 (5.6) | Reference | 118 (2.2) | Reference |
| IIIM and IIINM | 708 (11.3) | 2.14 (1.78 to 2.57)*** | 251 (3.9) | 1.60 (1.20 to 2.15)** |
| IV and V | 398 (12.6) | 2.46 (1.98 to 3.05)*** | 151 (4.7) | 1.76 (1.27 to 2.43)** |
| Ethnicity | ||||
| White | 1463 (10.2) | Reference | 517 (3.6) | Reference |
| Black | 49 (12.1) | 0.91 (0.57 to 1.45) | 23 (5.5) | 1.07 (0.57 to 1.99) |
| Indian subcontinent | 36 (7.0) | 0.45 (0.26 to 0.79)** | 8 (1.6) | 0.53 (0.18 to 1.53) |
| Other | 40 (11.2) | 0.91 (0.55 to 1.52) | 23 (6.3) | 1.67 (0.90 to 3.13) |
| Psychosis | 7 (14.0) | 0.51 (0.17 to 1.55) | 8 (15.8) | 1.49 (0.46 to 4.77) |
| Alcohol dependence | 371 (35.3) | 2.39 (1.92 to 2.96)*** | 186 (17.5) | 2.98 (2.21 to 4.03)*** |
| Anxiety disorder | 375 (16.2) | 1.87 (1.54 to 2.26)*** | 182 (7.7) | 2.03 (1.52 to 2.72)*** |
| ASPD | 246 (46.2) | 3.43 (2.57 to 4.60)*** | 159 (29.4) | 6.45 (4.59 to 9.06)*** |
| Depressive episode | 79 (18.9) | 1.51 (1.02 to 2.22)* | 43 (9.8) | 1.41 (0.77 to 2.56) |
Main associations of drug dependence with violence
Table 49 shows unadjusted and adjusted associations of drug dependence with all key measures of violence. Drug dependence was significantly associated with all violent outcomes in univariate models (all p < 0.001). After adjustments, drug dependence was still an important source of risk for most outcomes of violence, including incidents of violence while intoxicated, repeated violence, violence leading to injuries and violence in which the police were involved.
| Outcomes | n (%) violent | OR (CI 95%) | AORa (CI 95%) |
|---|---|---|---|
| Any violence | 1596 (10.1) | 9.87 (7.88 to 12.35)*** | 2.32 (1.74 to 3.10)*** |
| Violence while intoxicated | 685 (4.4) | 14.63 (11.40 to 18.78)*** | 2.61 (1.89 to 3.62)*** |
| Severity of violence | |||
| Minor violence | 655 (4.2) | 3.17 (2.23 to 4.50)*** | 1.03 (0.67 to 1.56) |
| Five or more violent incidents | 335 (2.1) | 11.49 (8.14 to 16.22)*** | 2.36 (1.53 to 3.62)*** |
| Victim injured | 505 (3.2) | 11.40 (8.61 to 15.10)*** | 2.17 (1.49 to 3.17)*** |
| Perpetrator injured | 514 (3.3) | 10.61 (7.94 to 14.19)*** | 1.99 (1.33 to 2.97)*** |
| Police involved | 431 (2.7) | 11.42 (8.58 to 15.19)*** | 2.65 (1.85 to 3.80)*** |
| Victim of violence | |||
| Iintimate partner | 252 (1.6) | 7.05 (4.88 to 10.18)*** | 1.77 (1.07 to 2.92)* |
| Family member | 153 (1.0) | 4.10 (2.22 to 7.58)*** | 1.04 (0.50 to 2.14) |
| Friend | 312 (2.0) | 11.46 (7.86 to 16.70)*** | 3.18 (2.02 to 5.01)*** |
| Person known | 511 (3.2) | 9.91 (7.26 to 13.52)*** | 2.65 (1.77 to 3.95)*** |
| Stranger | 783 (5.0) | 7.95 (6.20 to 10.21)*** | 1.64 (1.19 to 2.25)** |
| Police | 88 (0.6) | 14.80 (8.56 to 25.59)*** | 2.35 (1.13 to 4.87)* |
| Other | 108 (0.7) | 5.97 (3.25 to 10.98)*** | 1.52 (0.60 to 3.82) |
| Location of violent incident | |||
| Own home | 292 (1.9) | 7.18 (5.06 to 10.19)*** | 2.33 (1.44 to 3.77)*** |
| Someone else’s home | 138 (0.9) | 9.84 (5.91 to 16.37)*** | 2.52 (1.33 to 4.79)** |
| Street | 909 (5.8) | 9.92 (7.77 to 12.67)*** | 2.18 (1.59 to 3.00)*** |
| Bar/pub | 541 (3.4) | 9.52 (7.19 to 12.61)*** | 1.72 (1.20 to 2.48)** |
| Workplace | 101 (0.6) | 5.64 (2.94 to 10.84)*** | 1.60 (0.76 to 3.37) |
| Other | 241 (1.5) | 7.40 (4.80 to 11.43)*** | 2.41 (1.35 to 4.29)** |
Drug dependence also significantly increased the likelihood of violence towards intimate partners, friends, persons known, strangers and the police and the likelihood of violence taking place in all locations, with the exception of the workplace.
Violence classes and drug dependence
Latent class models derived five violence classes for men and three violence classes for women in this joint data set. The development of these LCAs is described in detail in Chapter 2. Tables 50 and 51 show the associations between individuals with drug dependence and the violence classes for men and women respectively.
| Violence typologies | Drug dependence, n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 169 (2.6) | Reference | Reference |
| Minor violence | 48 (10.6) | 1.95 (1.22 to 3.13)** | 1.57 (0.96 to 2.58) |
| Violence towards known persons | 72 (24.4) | 6.38 (4.09 to 9.94)*** | 3.63 (2.21 to 5.94)*** |
| Fighting with strangers | 54 (17.4) | 2.98 (1.83 to 4.86)*** | 1.67 (0.98 to 2.83) |
| Serious repetitive violence | 48 (39.8) | 11.16 (6.31 to 19.72)*** | 4.73 (2.61 to 8.56)*** |
| Violence typologies | Drug dependence, n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 127 (1.7) | Reference | Reference |
| General violence | 43 (15.4) | 4.94 (3.02 to 8.07)*** | 3.16 (1.84 to 5.43)*** |
| Intimate/family violence | 9 (7.0) | 2.01 (0.87 to 4.65) | 1.06 (0.43 to 2.60) |
In men, the ‘no violence’ class had the lowest prevalence of drug dependence at 2.6%. The highest prevalence of drug dependence was observed for class 5 (‘serious repetitive violence’) at 39.8%. Following adjustments, drug dependence was associated with more than a threefold increase in the odds of violence towards known persons and almost a fivefold increase of belonging in the odds of serious repetitive violence (see Table 50).
The female distribution of drug dependence across the classes was 1.7% for the ‘no violence’ class, 15.4% for the ‘general violence’ class and 7.0% for the ‘intimate/family violence’ class. Multinomial logistic regression models to estimate associations between violence latent classes and drug dependence showed a threefold increase in the odds of general violence compared with no violence (see Table 51).
Discussion
We found that the demographic characteristics of drug-dependent individuals in these household surveys were very similar to the demographic characteristics of those who were violent. As with individuals who report violence, drug-dependent individuals tend to be younger men who are single or separated from their partner, from lower social classes and with comorbid alcohol dependence and ASPD. The odds of reporting any violence in the past 5 years were more than doubled with drug dependence and it was unsurprising, in view of their dependence on drugs, that individuals were more likely to report violence when intoxicated. All levels of severity of violence were increased, including multiple incidents, incidents in which a victim was injured and incidents in which the perpetrator was injured. More importantly, individuals with drug dependence were more likely to report violence in which the police became involved. All victim types were reported except for members of the family. Friends and persons known showed the highest odds of association together with the police.
Because we do not have details of individual incidents, it cannot be confirmed whether these violent incidents corresponded to intoxication with drugs, situations in which drugs were bought and sold or situations in which altercations with drug dealers or customers had occurred or whether certain individuals had a pre-existing violent propensity that predisposed them both to violence and to abusing drugs to the extent that they became dependent on them. It is probable that all of these possibilities, in different combinations, had an effect on the associations with drug dependence. The level of police involvement with drug-dependent individuals in the study would suggest that they had regularly come into conflict with others and that the police had become involved. Alternatively, the police had become involved because of their drug-taking or other criminal activities such as theft and burglary, to obtain money for drugs. The strong association with friends and persons known to these individuals suggested that violence had occurred in a social milieu with other drug-abusing individuals, outside of the home. Nevertheless, risks of violence towards an intimate partner were also increased among this subgroup. On the other hand, the lack of association with other family members might suggest that drug misuse had caused these individuals to be estranged from close family members or that they were unable to support or maintain a family themselves.
Following adjustments, drug dependence was not found to be associated with classes 2 (‘minor violence’) and 4 (‘fighting with strangers’) among men. The strong association with serious repetitive violence among men (class 5) corresponded to the findings in Chapter 2 (see Study 2). Drug dependence may partly explain the wide range of potential victims of men in this class, with the risks increased for friends and persons known in the context of buying, selling and misusing drugs. The association with class 3 (‘violence towards known persons’) is also of considerable interest. Nearly one-quarter of these individuals were dependent on drugs. The higher level of divorced and separated men in this group and the finding that one-quarter were economically inactive, leading them to be poor at sustaining relationships and friendships, with many dependent on state benefits, may be partly explained by their drug dependence.
Similarities between women from class 2 and men from classes 3 and 5 were previously observed in Chapter 2 (see Study 2). More than one in six of this class of women were dependent on drugs.
There are four basic explanatory models for the relationship between drug use and violence: (1) substance use causes violence; (2) violence leads to substance use; (3) the relationship is reciprocal; and (4) the relationship is spurious, that is, it is coincidental or explained by a set of common causes. 183 Each model may be applicable to different subgroups of the population or to different incidents of drug-related violence. Unfortunately, a cross-sectional method does not allow exploration of these possibilities in any depth in this chapter. Nevertheless, these explanations are likely to have contributed to the findings.
Study 2: alcohol misuse
Objectives
The objectives this study were to:
-
investigate the prevalence of self-reported violence associated with alcohol dependence in a combined data set representative of the household population of Great Britain
-
investigate the independent associations of alcohol dependence with characteristics of violence, victim types and locations of violence
-
investigate the associations of alcohol dependence with a typology of violence in the general population.
Methods
Sample
The combined sample was drawn from the first phase of the NHPMS 2000 and the APMS 2007. The total sample included 15,734 men and women. Alcohol dependence was identified by a score of ≥ 20 on the AUDIT. 184
Statistical analyses
Weighted frequencies and proportions were reported for all categorical variables. Group associations between alcohol dependence and violence were established using binary logistic regression with the OR as the measure of magnitude. Multiple categorical predictor covariates were assigned a reference category against which other categories were contrasted. The violence typology for men and women described in Chapter 2 (see Study 1) were regressed on alcohol dependence using multinomial logistic regression.
All statistical analyses were adjusted by including covariates in each model simultaneously. Adjustments were made for demographic factors, psychosis, drug dependence, ASPD, depression and anxiety disorders. To adjust for the effects of selecting one individual per household and under-representation of certain subgroups, and to account for any deviation from selecting a simple random sample, all estimates were weighted. Details of the procedures used in weighting have been described previously. 45,82 All models employed robust SEs to adjust for clustering of individuals within postcodes. All analyses were performed using Stata.
Results
Demographic characteristics
Of the 15,734 respondents, 1596 (10.1%) reported any violence in the past 5 years. Table 52 shows that male sex, marital status other than married and social class lower than I and II were significantly associated with violence risk. Any age category > 34 years and being of Asian/Indian ethnicity were protective.
| Covariate | Any violence in the past 5 years (n = 1596; 10.1%) | Alcohol dependence (n = 1065; 6.7%) | ||
|---|---|---|---|---|
| n (%) reported | AORa (95% CI) | n (%) reported | AORa (95% CI) | |
| Sex | ||||
| Female | 406 (5.1) | Reference | 248 (3.1) | Reference |
| Male | 1190 (15.4) | 3.70 (3.14 to 4.36)*** | 817 (10.4) | 3.73 (3.13 to 4.45)*** |
| Age group (years) | ||||
| 16–34 | 1175 (22.4) | Reference | 602 (11.4) | Reference |
| 35–54 | 371 (6.3) | 0.32 (0.27 to 0.38)*** | 368 (6.2) | 0.73 (0.60 to 0.89)** |
| ≥ 55 | 51 (1.1) | 0.06 (0.04 to 0.08)*** | 95 (2.0) | 0.29 (0.22 to 0.38)*** |
| Marital status | ||||
| Married/cohabiting | 480 (4.7) | Reference | 376 (3.7) | Reference |
| Single | 973 (23.4) | 1.90 (1.59 to 2.26)*** | 558 (13.3) | 2.31 (1.91 to 2.79)*** |
| Divorced/separated | 144 (9.9) | 2.12 (1.69 to 2.66)*** | 131 (8.9) | 2.17 (1.74 to 2.72)*** |
| Social class | ||||
| I and II | 302 (5.6) | Reference | 314 (5.8) | Reference |
| IIIM and IIINM | 708 (11.3) | 2.10 (1.75 to 2.53)*** | 464 (7.3) | 1.18 (0.97 to 1.45) |
| IV and V | 398 (12.6) | 2.38 (1.92 to 2.95)*** | 227 (7.1) | 1.05 (0.84 to 1.31) |
| Ethnicity | ||||
| White | 1463 (10.2) | Reference | 1015 (7.0) | Reference |
| Black | 49 (12.1) | 0.85 (0.53 to 1.36) | 15 (3.6) | 0.38 (0.20 to 0.70)** |
| Indian subcontinent | 36 (7.0) | 0.43 (0.25 to 0.75)** | 8 (1.6) | 0.22 (0.09 to 0.52)** |
| Other | 40 (11.2) | 0.86 (0.51 to 1.46) | 20 (5.6) | 0.63 (0.34 to 1.16) |
| Psychosis | 7 (14.0) | 0.50 (0.15 to 1.69) | 10 (19.1) | 1.66 (0.63 to 4.37) |
| Drug dependence | 371 (35.3) | 2.63 (1.99 to 3.47)*** | 186 (32.5) | 2.83 (2.08 to 3.83)*** |
| Anxiety disorder | 276 (48.4) | 1.92 (1.60 to 2.32)*** | 294 (12.5) | 2.23 (1.82 to 2.72)*** |
| ASPD | 246 (46.2) | 3.14 (2.33 to 4.22)*** | 170 (31.4) | 2.48 (1.81 to 3.39)*** |
| Depression | 79 (18.9) | 1.53 (1.05 to 2.24)* | 70 (16.0) | 1.80 (1.24 to 2.61)** |
The overall weighted prevalence of alcohol dependence in the sample was 6.7%. Being male and of single or divorced/separated marital status increased the risk associations with alcohol dependence. Any age category > 34 years and being of Asian/Indian or black ethnicity had protective associations with alcohol dependence. Having an anxiety disorder, depression or ASPD or being dependent on drugs was associated with alcohol dependence in adjusted models and therefore highly comorbid in the study population.
Main associations of alcohol dependence with violence
Table 53 shows the unadjusted and adjusted associations of alcohol dependence with all key violent outcomes, victim types and locations of reported violence. Alcohol dependence was significantly associated with all violent outcomes in univariate models (all p < 0.001). After adjustments, alcohol dependence was still an important risk correlate for most descriptors of violence, including violence while intoxicated, repeated violence, violence leading to injuries and violence in which the police were involved.
| Outcomes | n (%) violent | OR (CI 95%) | AORa (CI 95%) |
|---|---|---|---|
| Any violence | 1596 (10.1) | 5.90 (4.97 to 7.00)*** | 2.19 (1.75 to 2.74)*** |
| Violence while intoxicated | 685 (4.3) | 11.09 (9.02 to 13.63)*** | 3.86 (2.98 to 5.01)*** |
| Severity of violence | |||
| Minor violence | 655 (4.2) | 2.91 (2.22 to 3.82)*** | 1.27 (0.92 to 1.75) |
| Five or more violent incidents | 335 (2.1) | 6.95 (5.05 to 9.56)*** | 1.88 (1.27 to 2.78)** |
| Victim injured | 505 (3.2) | 6.32 (4.91 to 8.13)*** | 2.00 (1.44 to 2.78)*** |
| Perpetrator injured | 514 (3.3) | 7.25 (5.75 to 9.14)*** | 2.68 (2.00 to 3.59)*** |
| Police involved | 431 (2.7) | 5.64 (4.29 to 7.41)*** | 1.81 (1.30 to 2.53)*** |
| Victim of violence | |||
| Intimate partner | 252 (1.6) | 4.96 (3.61 to 6.82)*** | 2.29 (1.49 to 3.52)*** |
| Family member | 153 (1.0) | 3.05 (1.82 to 5.11)*** | 1.48 (0.77 to 2.84) |
| Friend | 312 (2.0) | 5.65 (4.04 to 7.90)*** | 1.68 (1.11 to 2.53)* |
| Person known | 511 (3.2) | 4.38 (3.34 to 5.73)*** | 1.47 (1.05 to 2.06)* |
| Stranger | 783 (5.0) | 6.72 (5.38 to 8.40)*** | 2.23 (1.68 to 2.95)*** |
| Police | 88 (0.6) | 9.00 (5.42 to 14.95)*** | 2.37 (1.30 to 4.32)** |
| Other | 108 (0.7) | 2.92 (1.62 to 5.28)*** | 0.98 (0.46 to 2.08) |
| Location of violent incident | |||
| Own home | 292 (1.9) | 3.86 (2.83 to 5.27)*** | 1.68 (1.11 to 2.53)* |
| Someone else’s home | 138 (0.9) | 4.51 (2.66 to 7.66)*** | 1.22 (0.67 to 2.23) |
| Street/outdoors | 909 (5.8) | 5.68 (4.59 to 7.02)*** | 1.88 (1.43 to 2.46)*** |
| Bar/pub | 541 (3.4) | 9.73 (7.73 to 12.26)*** | 3.41 (2.54 to 4.59)*** |
| Workplace | 101 (0.6) | 3.93 (2.20 to 7.00)*** | 1.28 (0.71 to 2.32) |
| Other | 241 (1.5) | 2.96 (1.87 to 4.67)*** | 0.85 (0.48 to 1.50) |
Alcohol dependence also significantly increased the likelihood of violence towards intimate partners, friends, persons known, strangers and the police and the likelihood of violence taking place in all locations with the exception of someone else’s home and in the workplace.
Violence classes and alcohol dependence
Latent class models derived five violence classes for men and three violence classes for women in this joint data set. Development of these LCAs is described in detail in Chapter 2. Tables 54 and 55 include the associations between individuals with alcohol dependence and the violence classes for men and women respectively.
| Violence typologies | Alcohol n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 494 (7.5) | Reference | Reference |
| Minor violence | 82 (18.2) | 1.77 (1.24 to 2.54)** | 1.57 (1.08 to 2.28)* |
| Violence towards known persons | 91 (30.8) | 4.17 (2.89 to 6.01)*** | 3.10 (2.13 to 4.52)*** |
| Fighting with strangers | 86 (28.0) | 3.13 (2.15 to 4.55)*** | 2.45 (1.65 to 3.63)*** |
| Serious repetitive violence | 55 (45.5) | 5.90 (3.59 to 9.69)*** | 3.59 (2.07 to 6.21)*** |
| Violence typologies | Alcohol, n (%) | RRRa (95% CI) | RRRb (95% CI) |
|---|---|---|---|
| No violence | 193 (2.5) | Reference | Reference |
| General violence | 38 (13.4) | 2.68 (1.63 to 4.41)*** | 1.57 (0.87 to 2.84) |
| Intimate/family violence | 15 (12.1) | 3.14 (1.71 to 5.77)*** | 1.99 (0.99 to 3.98) |
In men, the ‘no violence’ class had the lowest prevalence of alcohol dependence at 7.5%. The highest prevalence of alcohol dependence was observed for class 5 (‘serious repetitive violence’) at 45.5%. After adjustments, alcohol dependence was associated with more than a threefold increase in the odds of violence towards known persons and serious repetitive violence. Alcohol dependence was also associated with minor violence and fighting with strangers (see Table 54).
The female distribution of alcohol dependence across the classes was 2.5% for the ‘no violence’ class, 13.4% for the ‘general violence’ class and 12.1% for the ‘intimate/family violence’ class. Adjusted multinomial logistic regression models to estimate associations between violence latent classes and alcohol dependence revealed no significant associations for the violence classes compared with the ‘no violence’ class (see Table 55).
Discussion
The demographic associations with both violence and alcohol dependence in the general population were similar and corresponded in part to those observed for drug dependence. Violent and alcohol-dependent individuals tended to be male, younger, single or separated and with comorbid drug dependence, anxiety disorder and ASPD. However, the association that we observed for drug dependence with lower social class was not observed for alcohol dependence. Furthermore, being of black and Indian subcontinent ethnic origin appeared to be protective for alcohol dependence and also for violence among individuals originating from the Indian subcontinent. As with drug dependence, it was an unsurprising finding that alcohol dependence was associated with violence when intoxicated. However, the odds of association were higher for dependence on alcohol than for dependence on drugs. Alcohol dependence was not associated with minor violent incidents and was strongly associated with more severe altercations with others, violence involving injuries and police involvement. The somewhat stronger association with the perpetrator being injured than with the victim being injured may correspond to previous observations in cases of homicide that, when a fight has occurred in which one of the participants is killed (typically an altercation between strangers when intoxicated in or outside a bar), the deceased is subsequently found to have consumed a higher level of alcohol than the person charged with the murder. 185 In general, studies of victims of violence in Denmark186 and the Netherlands187 have shown that between 40% and 50% of victims had been drinking, although Shepherd et al. 188 found that 74% of male victims in a British study had been drinking.
Victims of those with alcohol dependence were similar to victims of those with drug dependence, but with a stronger level of association for IPV. There was no association with violence towards family members and the level of violence towards friends and persons known showed a weaker association than for drug dependence, but with a strong association for violence towards strangers and police involvement. The locations of violence were fewer than for drug dependence, with violence occurring in the home of perpetrators, which would correspond to IPV, but also in the street/outdoors or in a bar/pub.
Alcohol misuse and its relationship to violence, particularly binge drinking, have generated increasing public and political concern in the UK following a marked increase in the number of licensed premises selling alcohol over the past 25 years189 and legislative changes relaxing the laws relating to the sale of alcohol. Research into alcohol-related disorder highlights the concentration of violent and public order offences in urban areas with high densities of licensed premises, which peaks at weekends. 44 This has emerged within the planned regeneration of certain inner-urban areas in the UK but where there is competition among licensed premises designed to accommodate large numbers of drinkers, resulting in cheaper alcohol. The financial resources available to the alcohol industry in the UK (facilitating more effective litigation to overcome objections of residents and regulations of local authorities), coupled with an inherent culture of binge drinking, have compounded these problems. 190
In a previous study83 of violence and psychiatric morbidity in the household population of Great Britain, the highest percentage of incidents and the high population-attributable risk were explained by individuals who engaged in hazardous drinking (27%). The prevalence of alcohol dependence in this previous study (involving one of the two data sets included in the present study) was a similar 7% of the population. However, many more of the population engage in hazardous drinking and therefore hazardous drinkers accounted for 56% of violent incidents over the study period. Nevertheless, the relatively small percentage of alcohol-dependent individuals was associated with a substantial 29% of all violent events. This would indicate that a targeted approach to alcohol-dependent individuals who are prone to become involved in violence might have a major impact on the overall level of violence in the population. 74
Alcohol dependence was associated with all classes of violence among men, but the associations with alcohol dependence were no longer significant following adjustments among women. These were surprising findings and did not correspond to the observation in study 1 that women involved in general violence (class 2) showed a significant association with drug dependence. Two factors may partly explain these differences. First, the relative proportions of men and women who are drug and alcohol dependent differs, with a larger proportion of men dependent on alcohol relative to women compared with those dependent on drugs. Second, we have previously referred to the ‘threshold’ hypothesis and the notion that women who develop antisocial behaviour surmount a higher threshold of risk than men and are therefore more severely afflicted. 86 We earlier showed that women were found to have raised thresholds of risk from affective and anxiety disorders and personality disorder. However, men in the general population of Great Britain were found to surmount a lower threshold of risk specifically from hazardous drinking and alcohol dependence than violent women. It was thought that the explanation for these raised thresholds of risk from heavy drinking could be explained primarily by the drinking culture of men in Great Britain. Many more men than women drank heavily. In this context it is probable that a large number of additional factors associated with heavy alcohol misuse, but not investigated in this study, characterised men but not women to account for these sex differences. However, these were not associated with drug dependence, with both men and women appearing to show relatively similar associations with violence.
Chapter 7 Childhood maltreatment and adult victimisation
Background
There is a strong overlap between violence and victimisation. Perpetrators and victims share significant characteristics and behaviours and are often the same people. 191–198 Posick199 has pointed out that, traditionally, research on violence has considered two separate groups of individuals: one group that harms others (offenders or perpetrators of violence) and one group that is harmed by others (victims). However, their common characteristics suggest that it is difficult to understand either violence or violent victimisation without understanding both.
Although many violent incidents occur with little or no contribution on the part of the victim, social interactions between the participants during violent incidents often suggest that there are many similarities and that victims can contribute to their own injury through their behaviour. Furthermore, individuals who are violent towards others also have a tendency to put themselves at greater risk.
Hinderlang193 has described how early systems of law and politics can provide a backdrop for a violent cycle of attack and retaliation. In some cultures it is considered necessary that victims seek revenge for wrongs committed against them or their family. In these social settings, a person may be the victim of an attack one day and the perpetrator the next. Ethnographic research on violence and disadvantaged neighbourhoods reveals that some individuals are enmeshed in the violent culture of offending and victimisation through retaliation. 152,200,201 This is particularly the case among gangs in such neighbourhoods. 202 Posick199 has described victims and offenders as similar groups of individuals in such contexts, who are involved in a ‘cycle of violence’. The social setting may be the link between offending and victimisation. Who becomes a victim and who becomes an offender within a particular violent event may be based on the immediate social interaction. The initial offender may become the ultimate victim and vice versa. The difference between victims and offenders in such interactions may be obscure and both parties may escalate the violent situation by trying to save face through acting aggressively. 203 Some individuals may act tough and resort to violence if disrespected. However, because there are many individuals in certain locations, there are likely to be some ‘winners’ (who will be seen as perpetrators) and some ‘losers’ (who become victims). The next time similar individuals meet, roles may be reversed. 204
A further body of research that has proceeded independently but which shows close parallels with the above studies of adult perpetrator–victim interactions is the child to adult ‘cycle of violence’ hypothesis. This posits that being abused or neglected as a child increases the risk for delinquency, adult criminal behaviour and violent criminal behaviour. 205 Although abused and neglected children are generally at ‘high risk’ for social problems, not all succumb to these and protective factors can intervene in a child’s development, leading some to be ‘resilient’. These include dispositional attributes, environmental conditions, biological predispositions and positive events that mitigate against early negative experiences. 206 However, recent research was unable to confirm the cycle of violence hypothesis207 and a literature review has also highlighted methodological inconsistencies across studies. 205
In this chapter we first investigate a wider range of childhood exposures and psychopathology in adulthood than in previous studies to test the cycle of violence hypothesis. In addition to childhood experiences of physical abuse, sexual abuse and neglect, we also include witnessing domestic violence and being the victim of bullying. In the second study of victimisation and violence perpetration in adulthood, the cross-sectional methods used ultimately prevented us from examining the relationship between victims and perpetrators. Our investigation is, therefore, limited to both of these experiences as reported by the same individuals, with the intention of measuring their association at the population level. Study 2 therefore provides a basis for future epidemiological investigation.
Study 1: childhood maltreatment and perpetration of violence in adulthood
Objectives
The objectives of the study were to investigate:
-
whether or not direct associations exist between different types of early maltreatment and violence among young adult men in a representative national sample of young adult men
-
whether or not there is a linear increase in the proportion of violence associated with multiple forms of maltreatment
-
the associations between child maltreatment and a typology of violence in the general population.
Methods
Data collection
The second MMLS was a cross-sectional survey of young adult men aged 18–34 years (n = 5400) carried out in 2011 in Great Britain. The sampling methods and components, and measures of the survey, are described in Chapter 2 (see Study 1).
Measures
Early maltreatment was defined by affirmative responses to self-reported experiences before 16 years of age, including sexual abuse/assault, physical abuse, neglect, having been bullied, witnessing violence in the home and witnessing parents or carers fighting.
Statistical analyses
For descriptive purposes, absolute (n) and relative (%) frequencies were reported for all dichotomous/polytomous categorical variables.
We initially investigated associations between early maltreatment and violence in the past 5 years and estimated their independent effects by including all forms of maltreatment simultaneously. To estimate the impact of exposure to multiple adverse childhood events, we combined the binary maltreatment variables and plotted their frequencies against any violence in the past 5 years and explored whether or not there was a significant linear trend.
To control for differences between samples, survey type was included as a covariate in all analyses. We also used robust SEs to account for correlations within survey areas because of clustering within postcodes. An α level of 0.05 was adopted throughout. All analyses were performed in Stata.
Results
Demography and sampling
The weighted final sample included 5400 men aged 18–34 years, of whom 1360 (25.2%) reported being bullied, 585 (10.8%) reported witnessing violence at home, 571 (10.6%) reported witnessing parents/carers fighting, 135 (2.5%) reported experiencing sexual abuse, 337 (6.2%) reported experiencing physical abuse and 272 (5.0%) reported having being neglected before 16 years of age (Table 56).
| Covariate | Bullied, n (%) | Witnessed violence at home, n (%) | Witnessed parents/carers fighting, n (%) | Sexual abuse, n (%) | Physical abuse, n (%) | Neglect, n (%) |
|---|---|---|---|---|---|---|
| All participants | 1360 (25.2) | 585 (10.8) | 571 (10.6) | 135 (2.5) | 337 (6.2) | 272 (5.0) |
| Age group (years) | ||||||
| 18–24 (reference) | 513 (37.7) | 194 (33.1) | 215 (37.7) | 42 (31.3) | 114 (33.8) | 103 (37.7) |
| 25–34 | 847 (62.3) | 391 (66.9)** | 356 (62.3) | 93 (68.7) | 223 (66.2) | 169 (62.3) |
| Marital status | ||||||
| Married/cohabiting (reference) | 450 (33.2) | 190 (32.8) | 191 (33.8) | 36 (27.4) | 120 (35.9) | 89 (33.2) |
| Single | 850 (62.7) | 358 (61.9) | 345 (61.1) | 89 (67.4) | 188 (56.4) | 160 (59.4) |
| Divorced/separated | 55 (4.1) | 31 (5.3) | 29 (5.1) | 7 (5.2) | 26 (7.7)* | 20 (7.4)* |
| Social class | ||||||
| I and II (reference) | 147 (11.2) | 51 (9.1) | 48 (8.7) | 20 (15.4) | 34 (10.6) | 32 (11.9) |
| IIIM and IIINM | 362 (27.7) | 119 (21.1) | 119 (21.4) | 37 (29.0) | 70 (21.5) | 56 (21.0) |
| IV and V | 302 (23.1) | 137 (24.1) | 139 (25.0) | 19 (14.6)*a | 75 (23.0) | 62 (23.5) |
| Unemployed/NC | 496 (37.9) | 259 (45.8)* | 250 (45.0)* | 53 (41.0) | 147 (44.9) | 116 (43.6) |
| Ethnicity | ||||||
| White (reference) | 988 (72.7) | 443 (75.8) | 445 (77.9) | 72 (53.4) | 238 (70.6) | 156 (57.3) |
| Black | 184 (13.5) | 87 (14.9) | 74 (13.0) | 31 (22.7) | 58 (17.4) | 61 (22.4)* |
| Asian and other | 187 (13.8)*a | 54 (9.3)**a | 52 (9.1)***a | 32 (23.9) | 40 (12.0)*a | 55 (20.3) |
| Non-UK born | 114 (8.6)**a | 53 (9.3) | 57 (10.3) | 32 (24.4) | 38 (11.6) | 38 (14.5) |
| Survey type | ||||||
| Main (reference) | 603 (44.4) | 218 (37.3) | 229 (40.2) | 48 (35.8) | 125 (37.1) | 78 (28.7) |
| BME | 182 (13.4)**a | 66 (11.3) | 68 (11.9) | 18 (13.2)*a | 35 (10.3) | 39 (14.3) |
| DE | 197 (14.5) | 82 (14.1) | 97 (17.0) | 15 (10.8) | 55 (16.4) | 45 (16.4)* |
| Hackney | 161 (11.8)**a | 66 (11.2) | 47 (8.3)*a | 42 (31.0) | 66 (19.5) | 72 (26.4)** |
| Glasgow East | 216 (15.9) | 153 (26.2)*** | 129 (22.6) | 12 (9.1) | 56 (16.7) | 39 (14.2) |
Table 57 shows that, after adjusting for all other maltreatment types, those of Asian and ‘other’ ethnicity were less likely to report witnessing their parents fighting but more likely to report neglect. Men who were not born in the UK were less likely to report having been bullied. Compared with those in higher social classes (I and II), men from social classes IV and V were less likely to report sexual abuse. Older age was associated with reports of witnessing violence in the home.
| Covariate | Being bullied, AOR (95% CI) | Witness violence at home, AOR (95% CI) | Witness parents/carers fighting, AOR (95% CI) | Sexual abuse, AOR (95% CI) | Physical abuse, AOR (95% CI) | Neglect, AOR (95% CI) |
|---|---|---|---|---|---|---|
| Age group (years) | ||||||
| 18–24 (reference) | – | – | – | – | – | – |
| 25–34 | 1.04 (0.88 to 1.24) | 1.56 (1.16 to 2.08)** | 0.83 (0.64 to 1.07) | 1.32 (0.79 to 2.19) | 0.91 (0.65 to 1.27) | 0.76 (0.53 to 1.09) |
| Marital status | ||||||
| Married/cohabiting (reference) | – | – | – | – | – | – |
| Single | 1.06 (0.89 to 1.28) | 1.12 (0.83 to 1.53) | 0.82 (0.62 to 1.07) | 1.63 (0.98 to 2.70) | 0.69 (0.47 to 1.00) | 0.82 (0.55 to 1.24) |
| Divorced/separated | 0.96 (0.64 to 1.44) | 0.99 (0.49 to 2.02) | 0.84 (0.40 to 1.75) | 0.93 (0.37 to 2.31) | 1.73 (0.92 to 3.27) | 1.48 (0.72 to 3.05) |
| Social class | ||||||
| I and II (reference) | – | – | – | – | – | – |
| IIIM and IIINM | 1.14 (0.85 to 1.53) | 1.05 (0.65 to 1.71) | 0.94 (0.62 to 1.44) | 1.01 (0.50 to 2.06) | 0.86 (0.48 to 1.54) | 0.83 (0.48 to 1.43) |
| IV and V | 0.90 (0.66 to 1.22) | 1.29 (0.77 to 2.18) | 1.19 (0.78 to 1.80) | 0.41 (0.19 to 0.88)* | 0.92 (0.53 to 1.61) | 0.97 (0.55 to 1.71) |
| Unemployed/NC | 1.02 (0.77 to 1.37) | 1.42 (0.86 to 2.35) | 1.26 (0.84 to 1.89) | 0.89 (0.45 to 1.75) | 1.43 (0.80 to 2.55) | 1.27 (0.74 to 2.19) |
| Ethnicity | ||||||
| White (reference) | – | – | – | – | – | – |
| Black | 1.01 (0.75 to 1.37) | 1.32 (0.77 to 2.29) | 0.62 (0.38 to 1.02) | 1.77 (0.90 to 3.47) | 0.75 (0.42 to 1.34) | 1.84 (0.99 to 3.41) |
| Asian and other | 0.82 (0.59 to 1.13) | 0.65 (0.37 to 1.14) | 0.39 (0.22 to 0.68)** | 1.74 (0.90 to 3.34) | 0.70 (0.37 to 1.34) | 2.04 (1.21 to 3.46)** |
| Non-UK born | 0.62 (0.47 to 0.80)*** | 0.85 (0.52 to 1.38) | 1.39 (0.90 to 2.15) | 1.74 (0.89 to 3.37) | 1.06 (0.61 to 1.87) | 0.99 (0.61 to 1.60) |
| Survey type | ||||||
| Main (reference) | – | – | – | – | – | – |
| BME | 0.63 (0.44 to 0.90)* | 0.88 (0.46 to 1.69) | 1.24 (0.68 to 2.25) | 0.37 (0.15 to 0.91)* | 0.89 (0.39 to 2.00) | 0.93 (0.46 to 1.89) |
| DE | 0.95 (0.74 to 1.21) | 0.83 (0.57 to 1.21) | 1.31 (0.93 to 1.85) | 0.84 (0.39 to 1.81) | 1.10 (0.70 to 1.75) | 1.68 (1.05 to 2.70)* |
| Hackney | 0.60 (0.45 to 0.80)*** | 0.82 (0.50 to 1.35) | 0.62 (0.38 to 1.03) | 1.89 (1.11 to 3.21)* | 2.02 (1.13 to 3.61)* | 2.74 (1.55 to 4.85)** |
| Glasgow East | 0.76 (0.60 to 0.97)* | 2.07 (1.53 to 2.81)*** | 1.13 (0.79 to 1.62) | 0.47 (0.21 to 1.04) | 0.88 (0.55 to 1.39) | 1.11 (0.68 to 1.80) |
Early maltreatment and violence
Table 58 shows the frequencies of violent outcomes and associations between early maltreatment and the violent outcomes before adjusting for the other types of maltreatment but after adjustments for demography. Having been a victim of bullying and physical abuse and having witnessed violence in the home showed positive associations with all outcomes except minor violence. Witnessing parents/carers fighting was associated with all outcomes except violence towards an unspecified victim type (‘other’). Sexual abuse was associated with all outcomes except minor violence and violence towards a person known and an unspecified victim type (‘other’). Experiencing physical abuse was associated with all outcomes except minor violence. Experiencing neglect was associated with all outcomes except minor violence and violence towards an unspecified victim type (‘other’).
| Outcomes | Being bullied, n (%) | Witnessed violence at home, n (%) | Witnessed parents/carers fighting, n (%) | Sexual abuse, n (%) | Physical abuse, n (%) | Neglect, n (%) |
|---|---|---|---|---|---|---|
| Any violence | 607 (45.0)*** | 388 (66.4)*** | 346 (60.8)*** | 75 (55.8)*** | 214 (63.6)*** | 167 (61.9)*** |
| Violence while intoxicated | 322 (24.3)*** | 234 (41.7)*** | 208 (38.0)*** | 37 (29.9)*** | 134 (42.0)*** | 94 (36.4)*** |
| Severity | ||||||
| Minor violence | 117 (8.7) | 45 (7.8) | 61 (10.8)* | 8 (6.1) | 24 (7.0) | 18 (6.6) |
| Repeated violence | 111 (8.4)*** | 89 (16.2)*** | 72 (13.4)*** | 23 (17.5)*** | 61 (19.0)*** | 43 (16.9)*** |
| Victim injured | 270 (20.1)*** | 217 (37.2)*** | 188 (33.3)*** | 31 (23.1)*** | 120 (35.8)*** | 91 (33.7)*** |
| Perpetrator injured | 331 (24.6)*** | 219 (37.6)*** | 178 (31.4)*** | 42 (31.5)*** | 132 (39.5)*** | 89 (33.3)*** |
| Police involved | 180 (13.4)*** | 126 (21.6)*** | 111 (19.6)*** | 33 (25.0)*** | 91 (27.2)*** | 62 (23.2)*** |
| Victim of violence | ||||||
| Intimate partner | 104 (7.7)*** | 85 (14.6)*** | 70 (12.4)*** | 27 (20.2)*** | 54 (16.2)*** | 55 (20.7)*** |
| Family member | 98 (7.3)*** | 100 (17.2)*** | 89 (15.7)*** | 16 (12.2)*** | 51 (15.2)*** | 37 (13.8)*** |
| Friend | 205 (15.2)*** | 122 (20.9)*** | 127 (22.4)*** | 27 (20.2)*** | 85 (25.4)*** | 65 (24.1)*** |
| Person known | 210 (15.6)*** | 142 (24.3)*** | 138 (24.4)*** | 16 (11.7) | 71 (21.3)*** | 57 (21.4)*** |
| Stranger | 287 (21.3)*** | 184 (31.5)*** | 185 (32.7)*** | 32 (23.9)*** | 114 (34.1)*** | 85 (31.5)*** |
| Police | 59 (4.4)*** | 51 (8.8)*** | 40 (7.1)*** | 9 (6.6)*** | 34 (10.1)*** | 22 (8.1)*** |
| Other | 37 (2.7)* | 21 (3.7)* | 14 (2.5) | 5 (3.7) | 15 (4.6)*** | 7 (2.6) |
| Location of violent incident | ||||||
| Own home | 114 (8.5)*** | 106 (18.1)*** | 84 (14.8)*** | 15 (11.4)** | 59 (17.5)*** | 41 (15.3)*** |
| Someone else’s home | 113 (8.4)*** | 110 (18.9)*** | 96 (17.0)*** | 23 (17.3)*** | 60 (18.0)*** | 47 (17.7)*** |
| Street/outdoors | 353 (26.3)*** | 228 (39.0)*** | 214 (37.8)*** | 38 (28.6)*** | 128 (38.1)*** | 100 (37.3)*** |
| Bar/pub | 255 (19.0)*** | 174 (29.8)*** | 168 (29.6)*** | 34 (25.7)*** | 99 (29.4)*** | 80 (29.8)*** |
| Workplace | 30 (2.2)*** | 16 (2.7)** | 16 (2.8)*** | 8 (5.8)*** | 15 (4.5)*** | 11 (4.0)** |
Table 59 shows the independent associations between self-reported maltreatment before 16 years of age and all violence outcomes following adjustment for other forms of maltreatment. Sexual abuse was not associated with any of the violent outcomes. Having been bullied was associated with violence while intoxicated, incidents in which the perpetrator was injured and incidents in which the police were involved. Being bullied was also related to violence towards intimate partners, friends and known persons and violence in the home, in the street, in bars/pubs and in the workplace.
| Outcomes | Being bullied, AOR (95% CI) | Witnessed violence at home, AOR (95% CI) | Witnessed parents/carers fighting, AOR (95% CI) | Sexual abuse, AOR (95% CI) | Physical abuse, AOR (95% CI) | Neglect, AOR (95% CI) |
|---|---|---|---|---|---|---|
| Any violence | 1.43 (1.20 to 1.70)*** | 2.93 (2.21 to 3.89)*** | 1.67 (1.28 to 2.17)*** | 0.94 (0.52 to 1.70) | 1.55 (1.12 to 2.13)** | 1.75 (1.22 to 2.52)** |
| Violence while intoxicated | 1.51 (1.23 to 1.85)*** | 2.52 (1.85 to 3.41)*** | 1.78 (1.32 to 2.38)*** | 0.82 (0.44 to 1.54) | 1.96 (1.37 to 2.80)*** | 1.45 (1.00 to 2.11)* |
| Severity | ||||||
| Repeated violence | 1.30 (0.93 to 1.81) | 2.42 (1.54 to 3.80)*** | 1.38 (0.90 to 2.10) | 1.27 (0.69 to 2.36) | 2.25 (1.44 to 3.50)*** | 1.40 (0.87 to 2.24) |
| Victim injured | 1.10 (0.88 to 1.38) | 2.59 (1.94 to 3.46)*** | 1.57 (1.17 to 2.11)** | 0.76 (0.40 to 1.45) | 1.82 (1.27 to 2.61)** | 1.62 (1.12 to 2.34)* |
| Perpetrator injured | 2.00 (1.60 to 2.50)*** | 2.60 (1.90 to 3.56)*** | 1.32 (0.97 to 1.79) | 1.00 (0.57 to 1.74) | 1.79 (1.26 to 2.53)** | 1.15 (0.77 to 1.71) |
| Police involved | 1.40 (1.07 to 1.83)* | 1.83 (1.24 to 2.68)** | 1.28 (0.88 to 1.87) | 1.71 (0.94 to 3.11) | 2.30 (1.52 to 3.48)*** | 1.55 (0.98 to 2.46) |
| Minor violence | 1.26 (0.96 to 1.65) | 0.82 (0.52 to 1.30) | 1.61 (1.07 to 2.43)* | 0.90 (0.41 to 1.98) | 0.75 (0.43 to 1.31) | 0.96 (0.55 to 1.69) |
| Victim of violence | ||||||
| Intimate partner | 1.67 (1.18 to 2.36)** | 3.08 (1.84 to 5.14)*** | 1.69 (1.04 to 2.76)* | 1.89 (0.93 to 3.85) | 1.29 (0.77 to 2.16) | 2.23 (1.40 to 3.55)*** |
| Family member | 1.30 (0.88 to 1.92) | 3.66 (2.32 to 5.78)*** | 2.24 (1.44 to 3.47)*** | 0.85 (0.40 to 1.81) | 1.38 (0.80 to 2.36) | 1.24 (0.72 to 2.13) |
| Friend | 1.85 (1.43 to 2.38)*** | 1.32 (0.92 to 1.90) | 2.17 (1.57 to 3.01)*** | 1.09 (0.61 to 1.96) | 1.56 (1.06 to 2.28)* | 1.62 (1.07 to 2.47)* |
| Person known | 1.57 (1.23 to 2.00)*** | 1.89 (1.32 to 2.71)*** | 1.90 (1.34 to 2.69)*** | 0.62 (0.32 to 1.23) | 1.14 (0.77 to 1.69) | 1.60 (1.04 to 2.45)* |
| Stranger | 1.25 (1.00 to 1.57) | 1.60 (1.18 to 2.17)** | 1.96 (1.46 to 2.63)*** | 0.82 (0.46 to 1.47) | 1.65 (1.14 to 2.40)** | 1.53 (1.05 to 2.24)* |
| Police | 1.30 (0.87 to 1.94) | 2.95 (1.68 to 5.19)*** | 1.01 (0.56 to 1.82) | 1.00 (0.41 to 2.42) | 2.07 (1.20 to 3.59)** | 1.17 (0.62 to 2.21) |
| Other | 1.50 (0.88 to 2.55) | 1.63 (0.86 to 3.09) | 0.64 (0.33 to 1.21) | 1.39 (0.52 to 3.67) | 2.51 (1.32 to 4.78)** | 0.76 (0.34 to 1.72) |
| Location of violent incident | ||||||
| Own home | 1.50 (1.07 to 2.09)* | 4.08 (2.59 to 6.43)*** | 1.59 (1.04 to 2.45)* | 0.74 (0.36 to 1.49) | 1.70 (1.04 to 2.77)* | 1.06 (0.63 to 1.81) |
| Someone else’s home | 1.12 (0.81 to 1.54) | 2.69 (1.84 to 3.94)*** | 1.67 (1.11 to 2.50)* | 1.79 (0.80 to 4.01) | 1.45 (0.92 to 2.30) | 1.46 (0.90 to 2.38) |
| Street/outdoors | 1.56 (1.28 to 1.90)*** | 2.10 (1.55 to 2.85)*** | 1.77 (1.34 to 2.32)*** | 0.79 (0.45 to 1.37) | 1.40 (0.99 to 1.98) | 1.61 (1.13 to 2.31)** |
| Bar/pub | 1.36 (1.08 to 1.72)** | 1.83 (1.30 to 2.58)*** | 1.91 (1.40 to 2.59)*** | 1.22 (0.68 to 2.22) | 1.21 (0.84 to 1.74) | 1.79 (1.25 to 2.57)** |
| Workplace | 1.94 (1.05 to 3.58)* | 1.09 (0.38 to 3.09) | 1.52 (0.58 to 4.01) | 1.90 (0.67 to 5.40) | 2.00 (0.74 to 5.44) | 1.43 (0.58 to 3.50) |
Having witnessed violence in the home was independently associated with all violent outcomes except minor violence, all victim types except friends and all locations except the workplace. Having witnessed parents/carers fighting was independently associated with violence while intoxicated, victim injury and minor violence, all victim types except the police and all locations except the workplace. Physical abuse was independently associated with all violent outcomes except minor violence, violence towards a friend, a stranger and the police and violence in the home. Neglect was independently associated with violence while intoxicated, victim injury, IPV, violence to friends, persons known and strangers and violence in the street/outdoors and in bars or pubs.
Multiple forms of maltreatment and violence
Figure 3 shows the change in the proportion of violence responders by number of early maltreatment types. Multiple forms of maltreatment were endorsed by young men in the sample. There was evidence of a linear trend in these weighted proportions (F = 71.2; p < 0.001).
FIGURE 3.
Impact of multiple maltreatment types on any violence in the last 5 years among young men.
Discussion
We confirmed that child maltreatment is a strong predictor of violent behaviour in adulthood among young adult men in the UK population. Our survey also revealed some important epidemiographic trends that require investigation. First, there were important demographic differences among those who reported early maltreatment. We examined typical demographic factors in the survey. However, we included four boost surveys: black and minority ethnic men, men of lower social class who were unemployed, the London borough of Hackney and Glasgow East. Hackney and Glasgow East contained very unusual populations with concentrations of severe socioeconomic deprivation. Hackney has one of the largest black and minority ethnic populations in the UK. Our findings therefore suggest the possibility that demographic and cultural influences, together with a concentration of social problems that affect children, are found together and interact in certain geographical areas.
The findings do not explain the mechanism but it would appear that men of South Asian and other black and minority ethnic origin, together with those who were not born in the UK, were less likely to report bullying in childhood. Similarly, significantly fewer men from the black and minority ethnic and Hackney boost surveys reported bullying before the age of 15 years. It is of interest that black and minority ethnic men reported less perpetration of violence towards others and perpetration/victimisation in adulthood (see Study 2).
Examining each subcategory of early maltreatment, men in the older age range (25–34 years) reported more childhood maltreatment than younger men (18–24 years). Whether there has been a change in the level of violence over time within families in the UK or whether this finding represents recall bias cannot be evaluated from this cross-sectional survey, but this is an important question for future research. However, the finding that more unemployed men had witnessed violence in the home and their parents/carers fighting suggested that these childhood experiences or associated disadvantage had a detrimental effect on later life success. It was of some interest that significantly more men from Glasgow East, the most socioeconomically deprived area of the UK, and with an almost entirely white population, reported the highest levels of violence in the home during childhood, suggesting the importance of area-level effects.
Difficulties in sustaining relationships in adulthood may be linked to physical abuse and neglect from parents/carers during childhood. Neglect was reported significantly more often by men of black ethnicity, men of lower social class, the unemployed and men from Hackney. Asian and other black and minority ethnic men appeared significantly less likely to report physical abuse, suggesting different parenting practices among different ethnic subgroups.
Examining adult outcomes of violence, lack of an association between childhood sexual abuse and violence in adulthood was the most striking observation. Sexual abuse has often been combined with other forms of maltreatment in previous studies, which prevents conclusions being drawn on the independent effects on Iater violence. However, childhood sexual abuse has been found to increase the risk of adult psychopathology208,209 and is associated with self-harm210,211 and with an increased risk of repeated sexual and other forms of victimisation (rather than perpetration) in adulthood, in samples that have included women. 212–214
Witnessing violence at home showed the strongest odds of association with adult outcomes of violence and a range of victims of violence. The range of different victims would explain the corresponding range of different locations of violence. However, childhood physical abuse was also independently associated with a range of different victims and more serious forms of violence towards others. A literature review has concluded that early physical abuse is the most consistent predictor of youth violence, particularly when compounded by additional forms of maltreatment. 215
Finally, we observed that the greater the number of early types of maltreatment experienced, the greater the proportion of men reporting violence towards others in adulthood. When all six forms of maltreatment were reported the above trend ceased to exist. This may be because of the small numbers of men in this subgroup and lack of power. Nevertheless, the linear trend observed suggests a dose–response relationship between childhood maltreatment and violence in adulthood, suggesting a causal relationship that requires confirmation in longitudinal studies.
Study 2: childhood maltreatment, victimisation and violence perpetration in adulthood
Objectives
The objectives of this study were to:
-
investigate the prevalence of victims of violence, violent perpetrators and victims/perpetrators in a representative sample of young men aged 18–34 years
-
compare victims and perpetrators with young men who either had never been a victim of violence or had acted violently in the past 5 years
-
identify characteristics of victims and perpetrators of violence.
Methods
Data collection
The second MMLS was a cross-sectional survey of young adult men aged 18–34 years (n = 5400) carried out in 2011 in Great Britain. This survey is described in Chapter 2 (see Study 1).
Measures
Early maltreatment and adversity was defined by affirmative responses to self-reported experiences before the age of 16 years, including having been taken to local authority care, sexual abuse/assault, physical abuse, neglect, having been bullied, witnessing violence in the home and witnessing parents or carers fighting.
Participants were classified based on their self-report of ever acting violently towards another person or ever having been a victim of violence. They were then divided into four groups: no violence, victim of violence, perpetrator of violence and victims/perpetrators.
Statistical analyses
For descriptive purposes, absolute (n) and relative (%) frequencies were reported for all dichotomous/polytomous categorical variables.
We investigated associations between demographic characteristics, mental health problems and a series of childhood adversities and mutually exclusive categories of victims of violence, perpetrators of violence and victims/perpetrators, with no violence as the reference. We fitted multinomial logistic regression models with categories of victimisation and perpetration regressed on all demographic correlates, mental health problems and childhood adversity.
To control for differences between samples, survey type was included as a covariate in all analyses. We also used robust SEs to account for correlations within survey areas because of clustering within postcodes. An α level of 0.05 was adopted throughout. All analyses were performed in Stata.
Results
Demography and sampling
The weighted final sample included 5400 men aged 18–34 years, of whom 2751 (54.1%) had not been violent in the past 5 years and had not been a victim of violence, 161 (3.2%) reported that they had been a victim but not a perpetrator of violence, 1491 (29.3%) reported perpetration of violence but not victimisation and 680 (13.4%) reported both victimisation and perpetration of violence in adulthood (Table 60).
| Survey type | No violence, n (%) | Victim, n (%) | Perpetrator, n (%) | Victim/perpetrator, n (%) |
|---|---|---|---|---|
| Main | 950 (48.7) | 59 (3.0) | 669 (34.3) | 272 (14.0) |
| BME | 709 (69.3) | 25 (2.5) | 241 (23.6) | 48 (4.7) |
| DE | 277 (44.8) | 16 (2.5) | 216 (34.8) | 111 (17.8) |
| Hackney | 464 (64.7) | 21 (2.9) | 174 (24.2) | 59 (8.2) |
| Glasgow East | 350 (45.3) | 41 (5.3) | 191 (24.7) | 191 (24.7) |
| Total sample | 2751 (54.1) | 161 (3.2) | 1491 (29.3) | 680 (13.4) |
Victims of violence were less likely to be single and were over-represented in the Glasgow East boost survey (Tables 61 and 62). Perpetrators were less likely to be from social classes IV and V and more were born in the UK. In contrast, South Asian and other ethnic groups and men from Hackney and Glasgow East were less likely to be perpetrators. Victims/perpetrators were significantly older, divorced or separated, from lower social classes and unemployed and more likely to be born in the UK. Victims/perpetrators were under-represented in Hackney but were over-represented in Glasgow East.
| Demographic characteristics | No violence, n (%) | Victim, n (%) | Perpetrator, n (%) | Victim/perpetrator, n (%) |
|---|---|---|---|---|
| All participants | 2751 (54.1) | 161 (3.2) | 1491 (29.3) | 680 (13.4) |
| Age group (years) | ||||
| 18–24 | 1076 (39.1) | 65 (40.1) | 572 (38.4) | 218 (32.1) |
| 25–34 | 1675 (60.9) | 97 (59.9) | 919 (61.7) | 462 (67.9) |
| Marital status | ||||
| Married/cohabiting/widowed | 902 (33.3) | 62 (38.8) | 504 (34.0) | 218 (32.1) |
| Single | 1727 (63.7) | 90 (56.5) | 915 (61.7) | 422 (62.2) |
| Divorced/separated | 82 (3.0) | 8 (4.7) | 64 (4.3) | 39 (5.7) |
| Social class | ||||
| I and II | 338 (12.9) | 17 (10.4) | 192 (13.7) | 49 (7.6) |
| IIIM and IIINM | 717 (27.4) | 50 (31.3) | 365 (26.0) | 167 (25.6) |
| IV and V | 709 (27.1) | 33 (20.7) | 344 (24.5) | 174 (26.6) |
| Unemployed | 852 (32.6) | 60 (37.6) | 502 (35.8) | 263 (40.2) |
| Non-UK born | 467 (17.3) | 16 (9.9) | 151 (10.4) | 43 (6.5) |
| Ethnicity | ||||
| White | 1526 (55.6) | 114 (70.5) | 1035 (69.5) | 573 (84.3) |
| Black | 414 (15.1) | 21 (13.0) | 221 (14.9) | 66 (9.7) |
| South Asian and other | 805 (29.3) | 27 (16.6) | 234 (15.7) | 41 (6.0) |
| Covariates | No violence (reference) | Victim, RRRa (95% CI) | Perpetrator, RRRa (95% CI) | Victim/perpetrator, RRRa (95% CI) |
|---|---|---|---|---|
| Age group (years) | ||||
| 18–24 | Reference | – | – | – |
| 25–34 | – | 0.82 (0.55 to 1.22) | 1.05 (0.89 to 1.25) | 1.44 (1.16 to 1.78)** |
| Marital status | ||||
| Married/cohabiting/widowed | Reference | – | – | – |
| Single | – | 0.62 (0.39 to 0.97)* | 0.95 (0.79 to 1.14) | 1.06 (0.84 to 1.33) |
| Divorced/separated | – | 1.23 (0.52 to 2.93) | 1.30 (0.86 to 1.98) | 1.71 (1.05 to 2.78)* |
| Social class | ||||
| I and II | Reference | – | – | – |
| IIIM and IIINM | – | 1.24 (0.65 to 2.33) | 0.79 (0.61 to 1.04) | 1.44 (0.97 to 2.15) |
| IV and V | – | 0.81 (0.43 to 1.54) | 0.78 (0.61 to 1.00)* | 1.53 (1.02 to 2.30)* |
| Unemployed | – | 1.27 (0.67 to 2.42) | 0.96 (0.74 to 1.25) | 1.86 (1.25 to 2.77)** |
| Non-UK born | – | 0.80 (0.35 to 1.81) | 0.74 (0.57 to 0.97)* | 0.67 (0.46 to 0.98)* |
| Ethnicity | ||||
| White | Reference | – | – | – |
| Black | – | 1.01 (0.44 to 2.32) | 1.05 (0.77 to 1.41) | 0.86 (0.55 to 1.34) |
| South Asian + other | – | 0.69 (0.27 to 1.78) | 0.64 (0.48 to 0.85)** | 0.30 (0.18 to 0.50)*** |
| Survey type | ||||
| Main | Reference | – | – | – |
| BME | – | 0.82 (0.30 to 2.22) | 0.68 (0.48 to 0.96)* | 0.51 (0.30 to 0.87)* |
| DE | – | 0.93 (0.50 to 1.73) | 1.15 (0.91 to 1.47) | 1.36 (0.99 to 1.88) |
| Hackney | – | 0.88 (0.40 to 1.92) | 0.66 (0.48 to 0.89)** | 0.59 (0.35 to 0.98)* |
| Glasgow East | – | 1.90 (1.18 to 3.07)** | 0.77 (0.60 to 0.99)* | 1.64 (1.23 to 2.19)** |
Categories of victimisation and mental health problems
After adjusting for demographics and other psychopathology, victims of violence reported experiencing long-standing mental health problems and admission to inpatient services and suffered from anxiety and psychotic symptoms. Drug dependence, alcohol dependence and depression were not significantly endorsed by victims of violence (Table 63).
| Exposures | No violence (reference) | Victim, RRRa (95% CI) | Perpetrator, RRRa (95% CI) | Victim/perpetrator, RRRa (95% CI) |
|---|---|---|---|---|
| Long-standing problems | – | 4.26 (2.38 to 7.62)*** | 2.50 (1.71 to 3.66)*** | 5.67 (3.82 to 8.41)*** |
| Adjustedb | – | 3.50 (1.65 to 7.42)** | 1.65 (1.07 to 2.52)* | 2.86 (1.76 to 4.65)*** |
| Admission to psychiatric hospital | – | 4.06 (1.65 to 9.97)** | 2.01 (1.27 to 3.18)** | 4.63 (2.95 to 7.28)*** |
| Adjustedb | – | 3.42 (1.07 to 10.95)* | 1.40 (0.86 to 2.31) | 2.65 (1.50 to 4.67)** |
| Anxiety disorder | – | 2.01 (1.22 to 3.32)** | 1.91 (1.48 to 2.46)*** | 3.57 (2.73 to 4.68)*** |
| Adjustedb | – | 2.05 (1.16 to 3.65)* | 1.59 (1.22 to 2.07)** | 2.32 (1.69 to 3.19)*** |
| Depression | – | 0.96 (0.57 to 1.63) | 0.77 (0.59 to 0.99)* | 0.88 (0.61 to 1.25) |
| Adjustedb | – | 0.74 (0.43 to 1.29) | 0.60 (0.45 to 0.81)** | 0.49 (0.32 to 0.73)*** |
| Psychosis | – | 6.03 (2.44 to 14.89)*** | 7.91 (4.32 to 14.50)*** | 14.09 (7.12 to 27.91)*** |
| Adjustedb | – | 3.72 (1.32 to 10.52)* | 3.89 (2.09 to 7.26)*** | 5.37 (2.57 to 11.24)*** |
| Alcohol dependence | – | 1.80 (1.01 to 3.22)* | 2.50 (1.82 to 3.44)*** | 3.83 (2.79 to 5.25)*** |
| Adjustedb | – | 1.17 (0.59 to 2.29) | 1.67 (1.23 to 2.25)** | 2.03 (1.41 to 2.92)*** |
| Drug dependence | – | 3.07 (0.60 to 15.84) | 16.91 (7.37 to 38.80)*** | 38.57 (16.30 to 91.26)*** |
| Adjustedb | – | 2.21 (0.40 to 12.23) | 10.88 (4.52 to 26.22)*** | 21.60 (8.61 to 54.17)*** |
Depression was under-represented among perpetrators of violence. All other mental health indicators were significantly increased among this group, with the exception of admission to inpatient services.
All mental health and psychopathology indicators were significantly more prevalent in the victim/perpetrator group. These men had the strongest odds of association with anxiety, psychosis and alcohol and drug dependence (see Table 63). Depression was less prevalent in this group.
Categories of victimisation and childhood adversities
After adjusting for demographics and other psychopathology, victims of violence were more likely to report having been bullied, witnessing violence in the home and having experienced physical abuse and neglect before the age of 16 years (Table 64). These men showed the strongest associations with having been bullied and experiencing physical abuse and neglect among all comparison groups.
| Exposures | No violence (reference) | Victim, RRRa (95% CI) | Perpetrator, RRRa (95% CI) | Victim/perpetrator, RRRa (95% CI) |
|---|---|---|---|---|
| Taken to local authority | – | 1.92 (0.64 to 5.73) | 3.76 (2.41 to 5.87)*** | 7.41 (4.70 to 11.70)*** |
| Adjustedb | – | 1.20 (0.38 to 3.76) | 2.98 (1.82 to 4.88)*** | 3.87 (2.22 to 6.77)*** |
| Been bullied | – | 7.21 (4.85 to 10.73)*** | 1.89 (1.56 to 2.28)*** | 5.92 (4.79 to 7.32)*** |
| Adjustedb | – | 5.25 (3.60 to 7.66)*** | 1.50 (1.23 to 1.83)*** | 3.69 (2.91 to 4.67)*** |
| Witnessed violence in the home | – | 6.60 (3.94 to 11.06)*** | 3.75 (2.77 to 5.08)*** | 13.32 (9.99 to 17.77)*** |
| Adjustedb | – | 2.35 (1.31 to 4.22)** | 1.79 (1.28 to 2.51)** | 4.32 (2.99 to 6.24)*** |
| Witnessed parents/carers fighting | – | 4.82 (2.74 to 8.47)*** | 4.16 (3.15 to 5.48)*** | 9.12 (6.88 to 12.09)*** |
| Adjustedb | – | 1.39 (0.76 to 2.54) | 2.51 (1.85 to 3.40)*** | 2.20 (1.51 to 3.21)*** |
| Sexual abuse | – | 7.32 (3.24 to 16.53)*** | 2.52 (1.36 to 4.68)** | 10.51 (5.90 to 18.72)*** |
| Adjustedb | – | 1.23 (0.40 to 3.77) | 0.93 (0.41 to 2.12) | 1.59 (0.64 to 3.95) |
| Physical abuse | – | 15.46 (8.01 to 29.84)*** | 3.68 (2.41 to 5.64)*** | 17.97 (11.78 to 27.40)*** |
| Adjustedb | – | 4.87 (2.53 to 9.38)*** | 1.71 (1.05 to 2.78)* | 3.93 (2.35 to 6.58)** |
| Neglect | – | 9.54 (4.69, 19.41)*** | 4.47 (2.90, 6.90)*** | 10.15 (6.56, 15.69)*** |
| Adjustedb | – | 2.77 (1.17 to 6.55)* | 2.18 (1.35 to 3.53)** | 1.79 (1.01 to 3.16)* |
All childhood adversity indicators were significantly increased among perpetrators of violence except for experiencing sexual abuse before the age of 16 years.
All childhood adversity indicators were significantly increased in the victim/perpetrator group except for experiencing sexual abuse before the age of 16 years. Among the four groups, victims/perpetrators showed the strongest associations with having been taken to local authority care and witnessing violence in the home (see Table 64).
Discussion
We found that few young adult men fitted the stereotype of an ‘innocent victim’ of violence, reporting victimisation but never having perpetrated violence towards others. However, most men had neither perpetrated violence nor been the victim of violence.
The distribution of men in our four categories across the components of the survey, particularly the additional boost surveys, is of some interest. For example, although men in the London borough of Hackney were found to have high levels of multiple problems, there was a high prevalence of those who reported no involvement in violence, which is of some interest. This is most likely a reflection of the unusual nature of the white men in the borough, many of whom were employed and in skilled occupations and of a higher social class, together with the relatively high proportion of men of Asian origin. This would suggest that a relatively small proportion of men in the borough are involved in violence as well as in repetitive and severe violence. In contrast, fewer men from the Glasgow East sample reported that they had been neither a victim nor a perpetrator of violence. We found that victimisation was highest in this subsample. In contrast, our representative UK sample of black and minority ethnic men was consistently less likely to report victimisation or perpetration. These trends were also confirmed following adjustments for demographic characteristics. Our findings suggest that men who were both victims and perpetrators tended to be older, divorced or separated, unemployed or of low social class and born in the UK and were unlikely to be of South Asian or other ethnic origin. Furthermore, victims/perpetrators were more likely to report long-standing mental health problems, admission to psychiatric hospital, anxiety disorder, symptoms of psychosis, alcohol dependence and drug dependence and were less likely to report depression. When examining their experiences of childhood maltreatment and poor care, victims/perpetrators showed the strongest association with being taken into local authority care and with witnessing violence in the family home. They were also more likely to report being bullied, witnessing parents/carers fighting, physical abuse and neglect. These findings suggest a constellation of maltreatment in childhood preceding violence and violent victimisation in adulthood, associated with poor physical and mental health in adulthood. Our findings suggest that this pattern or life course trajectory may be most prevalent in areas characterised by concentrated socioeconomic deprivation.
It was of some interest that men who reported being perpetrators of violence but not having experienced violent victimisation themselves made up a relatively large proportion of the overall sample. Furthermore, they were less likely to be participants from the boost surveys of the highly deprived inner-city areas of Hackney and Glasgow East. They were also less likely to be of black and minority ethnic origin, particularly South Asian and other, and were likely to be non-UK born and from social classes IV and V. They were more likely to report long-standing mental health problems than men who had not been involved in violence and report higher levels of anxiety disorder, psychotic symptoms, alcohol dependence and drug dependence. However, this group’s mental health problems did not result in them being admitted to a psychiatric hospital and they were less likely to be depressed. It is probable that individuals in the victim/perpetrator group were more reckless and impulsive than the perpetrator-only subgroup and thereby prone to become victims when they engaged in violent altercations with others. It is also possible that they carried out more violent assaults leading to a greater statistical chance that they would ultimately become a victim. Alternatively, their higher level of psychopathology, particularly substance dependence, may have rendered them more vulnerable during violent altercations to becoming a victim of violence. Ultimately, however, the cross-sectional method used in this study does not allow us to determine these possibilities.
In the case of the smallest group of individuals, victims and not perpetrators of violence, certain stereotyped notions of victimised men may well have applied. Victims were less likely to be single men. The finding that they were more likely to be found in the boost sample from Glasgow East could reflect a generally increased level of risk for violent victimisation when living in that geographical location. However, the strength of the odds of association between certain childhood factors suggested that there was a strong continuity in the risk of repeated victimisation from these childhood experiences to victimisation in adulthood. Men who were victims but not perpetrators showed stronger associations with being bullied in childhood, physical abuse and neglect than either perpetrators or victims/perpetrators. They were also more likely to report violence in the home during childhood than men who were not involved in violence. In adulthood, they showed the strongest associations with long-standing mental health problems, admission to a psychiatric hospital and psychotic symptoms and anxiety disorder, suggesting either vulnerability to adult victimisation as a result of their psychopathology or psychopathology as a result of their victimisation. However, being a victim was not specifically associated with alcohol dependence or drug dependence.
Finally, our study has confirmed that one of the best predictors of future victimisation is past victimisation. 216 Studies of sexual abuse among women show that those who have been abused as a child are two to three times more likely to be sexually assaulted later in life. 217 Our findings confirmed that men who have been physically abused and neglected in childhood are more likely to be violent towards others in adulthood but also to become victims themselves. However, we did not find an association between sexual abuse in childhood and adult victimisation. Finkelhor et al. 218 has argued that the re-victimisation and repeated victimisation literature has been limited because there is a tendency to consider victimisation in somewhat narrow terms, typically because studies have examined the recurrence of only one or a few kinds of victimisation, for example sexual abuse or violent crime. However, victimisation of one type, such as physical maltreatment, may create vulnerability for other kinds of victimisation such as bullying by peers or sexual victimisation. This would suggest that childhood maltreatment may act as a vulnerability for more than one type of victimisation in adulthood.
Another limitation to the re-victimisation literature is that it has tended to view victimisation as an event rather than a condition. Finkelhor et al. 218 argue that victimisation has often been treated as an unusual, individual event of a particularly traumatic nature; however, many victimisations are ongoing, as studies on bullying, child abuse and IPV make clear. Furthermore, studies of peer relationships among young children have increasingly suggested that some children become entrapped in the victimisation condition in which they are subjected to repeated attacks of different types from different children. 219,220 This type of victimisation proneness continues for years. Our findings suggest that this may certainly be the case for men who were victims/perpetrators but also for those who were victims. This suggests that an important focus of further investigation should be on the persistence of victimisation as a condition rather than simply the recurrence of certain kinds of victimisation events. Our findings also indicate that the ‘condition’ of revictimisation described by Finkelhor et al. 218 shows a strong association with psychiatric morbidity.
Chapter 8 Social deprivation and violence
Background
Almost two decades have passed since the World Health Assembly declared violence to be a major public health concern. 1 Numerous studies on the causes and consequences of violence have been carried out. One mechanism suggested as being responsible for violent behaviour is the influence of an individual’s neighbourhood of residence. Empirical research has consistently shown that intentional injuries are more prevalent among young people, in particular men, and people from adverse socioeconomic backgrounds. 221–223 There is also growing concern that people living in disadvantaged neighbourhoods experience a heightened risk of exposure to violence. 224,225
A study carried out in Scotland demonstrated that an increase in mortality as a result of assault was most pronounced in men living in the most deprived quintile of areas. 226 A Welsh study on youth violence showed that injury increased with increasing deprivation in cities and their feeder towns. 227 A retrospective review of NHS emergency department computer records demonstrated a very strong relationship between material deprivation and the risk of assault. 224 Finally, the ratio of the median rate of intentional to unintentional injuries increased steeply with economic deprivation in a study carried out in the USA. 228
Objectives
The objectives of this study were therefore to investigate whether or not:
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socioeconomic deprivation in a representative sample of young men in Great Britain was associated with violent behaviour and type of violence
-
socioeconomic deprivation in a representative sample of young men in Great Britain demonstrated a relationship with specific individuals as victims of violence
-
specific locations where violent incidents occurred differed depending on level of socioeconomic deprivation.
Methods
The sample under study was the second MMLS, described in Chapter 2. However, to investigate the effects of socioeconomic deprivation on violent outcome it was not possible to include the booster samples, as we wanted to include a representative sample of the underlying population. For subsequent analyses only the representative main survey with 2046 study participants was utilised.
Measures
The outcome measures of violence used in this study are described in Chapter 2.
Acorn (http://acorn.caci.co.uk/downloads/Acorn-user-guide.pdf) is a segmentation tool that categorises the UK’s population into demographic types and provides information about the level of socioeconomic deprivation by analysing significant social factors and population behaviour. Categories of Acorn were coded as ordinal variables (from 1 to 5, indicating an increase in socioeconomic deprivation) and were:
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Affluent achievers These are some of the most financially successful people in the UK. They live in wealthy, high-status rural, semirural and suburban areas of the country. Middle-aged or older people predominate as many have grown-up children no longer living at home and are wealthy retired. Some neighbourhoods contain large numbers of well-off families with school-aged children, particularly the more suburban locations.
-
Rising prosperity These are generally younger, well-educated and mostly prosperous people living in major towns and cities. Most are single or couples, with some yet to start a family and others having younger children. Often these are highly educated younger professionals moving up the career ladder.
-
Comfortable communities This category contains much of the average Great Britain, whether in the suburbs, smaller towns or the countryside. All life stages are represented in this category. Many areas have mostly stable families and families with grown-up children no longer living at home, especially in suburban or semi-rural locations. There are also comfortably off pensioners living in retirement areas around the coast or in the countryside and sometimes younger couples just starting out in their lives together.
-
Financially stretched This category contains a mix of traditional areas of Great Britain. Housing is often terraced or semi-detached, with a mix of lower-value owner-occupied housing and homes rented from the council or housing associations, including social housing developments specifically for the elderly. This category also includes student term-time areas. There tends to be fewer traditional married couples than usual and more single parents and single, separated and divorced people than the national average. Incomes tend to be well below the national average. Although some in this category have reasonably well-paid jobs, more people are in lower-paid administrative, clerical, semiskilled and manual jobs. Apprenticeships and O levels are the most common educational qualifications in this category. The levels of unemployment are above the national average as are the proportions of people claiming other benefits.
-
Urban adversity This category contains the most deprived areas of large and small towns and cities across the UK. Household incomes are low and nearly always below the national average. The number of people having difficulties with debt or having been refused credit is double the national average. The number claiming Jobseeker’s Allowance and other benefits is also well above the national average. Qualification levels are low and those in work are likely to be employed in semiskilled or unskilled occupations. The housing is a mix of low-rise estates, with terraced and semidetached houses, and purpose-built flats, including high-rise blocks. Properties tend to be small and there may be overcrowding. Over half of the housing is rented from the local council or a housing association. There is some private renting.
Statistical analyses
For descriptive purposes, absolute (n) and relative (%) frequencies were reported for dichotomous/polytomous categorical variables, means and SDs for variables on interval/ratio level.
Binary logistic regression was performed to examine the effects of socioeconomic deprivation (ordinal measure) on violent outcome (dichotomous). Appropriate weights were included in all analyses and analyses were adjusted for clustering within postcodes. No demographic variables were adjusted for in these analyses as the measure of socioeconomic deprivation was derived from factors including age, ethnicity, social class, education and employment status.
Results
The weighted mean age of the sample was 26.1 years. The majority of the sample was white (n = 1796, 88%) and nearly half of the sample was single (n = 819, 40.3%). In total, 11% (n = 229) had not achieved any educational qualifications. The distribution across social classes was as follows: 283 (13.8%) high, 627 (30.6%) medium and 449 (21.9%) low. Approximately one-third of the sample was not classifiable because of unemployment (310, 15.2%) or for other reasons (n = 378, 18.5%). The distribution of study participants across the different Acorn categories was as follows: (1) affluent achievers 14.8% (n = 302), (2) rising prosperity 15.1% (n = 308), (3) comfortable communities 28.1% (n = 574), (4) financially stretched 19.9% (n = 408) and (5) urban adversity 22.2% (n = 455). The highest prevalence was in the middle category.
Approximately one-third of the sample (n = 622, 31.7%) reported any violent behaviour in the past 5 years.
The weighted absolute and relative frequencies of types and victims of violence and location of violent incidents in each Acorn category of socioeconomic deprivation are provided in Table 65.
| Outcomes | Acorn category 1,a n (%) | Acorn category 2,a n (%) | Acorn category 3,a n (%) | Acorn category 4,a n (%) | Acorn category 5,a n (%) |
|---|---|---|---|---|---|
| Any violence | 70 (24.0) | 75 (24.9) | 185 (33.4) | 139 (36.2) | 152 (35.4) |
| Violence while intoxicated | 36 (12.4) | 36 (12.1) | 99 (17.9) | 82 (21.7) | 83 (19.8) |
| Severity of violence | |||||
| Repeated violence (five or more times) | 10 (3.5) | 8 (2.7) | 26 (4.7) | 22 (5.9) | 30 (7.1) |
| Victim injured | 29 (9.9) | 38 (12.5) | 95 (17.1) | 67 (17.6) | 82 (19.0) |
| Perpetrator injured | 26 (8.9) | 40 (13.4) | 84 (15.2) | 59 (15.4) | 73 (16.9) |
| Police involved | 18 (6.1) | 25 (8.4) | 53 (9.6) | 42 (10.8) | 52 (12.1) |
| Minor violence | 23 (8.0) | 19 (6.3) | 45 (8.1) | 42 (10.9) | 32 (7.5) |
| Victim of violence | |||||
| Intimate partner | 8 (2.6) | 7 (2.4) | 17 (3.0) | 14 (3.6) | 21 (4.8) |
| Family member | 3 (0.9) | 6 (2.1) | 29 (5.3) | 18 (4.7) | 22 (5.1) |
| Friend | 13 (4.6) | 22 (7.4) | 51 (9.2) | 30 (7.9) | 44 (10.1) |
| Someone known | 24 (8.0) | 27 (8.9) | 61 (10.9) | 52 (13.5) | 42 (9.8) |
| Stranger | 42 (14.2) | 45 (14.9) | 107 (19.3) | 75 (19.4) | 88 (20.3) |
| Police | 7 (2.2) | 7 (2.3) | 13 (2.4) | 18 (4.7) | 10 (2.3) |
| Location of violent incident | |||||
| Own home | 3 (1.0) | 6 (2.0) | 27 (4.8) | 18 (4.7) | 28 (6.5) |
| Someone else’s home | 6 (2.1) | 8 (2.8) | 25 (4.5) | 17 (4.5) | 23 (5.4) |
| Street/outdoors | 41 (13.9) | 45 (14.8) | 124 (22.4) | 92 (23.8) | 84 (19.4) |
| Bar/pub | 33 (11.3) | 38 (12.6) | 84 (15.1) | 65 (17.0) | 66 (15.3) |
| Workplace | 3 (0.9) | 1 (0.4) | 11 (1.9) | 4 (0.9) | 10 (2.1) |
| Other | 15 (5.2) | 15 (4.8) | 24 (4.3) | 16 (4.3) | 21 (4.9) |
To assure that associations of deprivation with violence were not accounted for by psychopathology, we tested whether or not there was an increase in the prevalence of mental illness with increasing deprivation. Of all mental disorders included (psychosis, anxiety, depression, alcohol abuse and dependence, drug abuse and dependence and ASPD), none was significantly associated with level of socioeconomic deprivation (p ≥ 0.05).
Type of violence
Apart from minor violence, all other types of violence were significantly associated with increasing level of socioeconomic deprivation as follows: any violence – adjusted OR (AOR) 1.16 (95% CI 1.07 to 1.26; p < 0.001), violence while intoxicated – AOR 1.18 (95% CI 1.07 to 1.30; p < 0.001), repeated violence (five or more times) – AOR 1.26 (95% CI 1.07 to 1.50; p = 0.006), victim injured – AOR 1.19 (95% CI 1.07 to 1.32; p = 0.001), perpetrator injured – AOR 1.16 (95% CI 1.04 to 1.28; p = 0.005) and police involved – AOR 1.18 (95% CI 1.04 to 1.34; p = 0.009).
Victims of violence
Level of socioeconomic deprivation predicted violence against a family member (AOR 1.33, 95% CI 1.13 to 1.57; p = 0.001), friends (AOR 1.16, 95% CI 1.03 to 1.31; p = 0.017) and strangers (AOR 1.12, 95% CI 1.02 to 1.23; p = 0.022). IPV (AOR 1.20, 95% CI 0.97 to 1.48; p = 0.093) and violence against someone known (AOR 1.08, 95% CI 0.96 to 1.21; p = 0.206) and the police (AOR 1.09, 95% CI 0.88 to 1.34; p = 0.426) were not associated with socioeconomic deprivation.
Location of violent incidents
Locations of violence significantly predicted by socioeconomic deprivation were the perpetrator’s home (AOR 1.44, 95% CI 1.21 to 1.71; p < 0.001), someone else’s home (AOR 1.24, 95% CI 1.05 to 1.47; p = 0.014) and streets/locations outdoors (AOR 1.12, 95% CI 1.03 to 1.22; p = 0.010). No association was found with bars/pubs (AOR 1.10, 95% CI 0.99 to 1.22; p = 0.072), the workplace (AOR 1.26, 95% CI 0.93 to 1.70; p = 0.135) and other locations (AOR 1.09, 95% CI 0.88 to 1.34; p = 0.426).
Discussion
Our findings confirm that level of socioeconomic deprivation is associated with risk of violence towards others in a representative sample of young British men. However, this association was found only with more serious forms of violence indicated by repetition, injury of perpetrator and/or victim and police involvement. In total, 8% of young men living in the most affluent parts of the country reported committing minor violent acts in the past 5 years, a similar prevalence to that among those living in the most deprived areas. This may correspond to the lack of association with IPV, indicating that violence towards spouses is independent of socioeconomic status. These findings contradict previous research in which socioeconomic deprivation on the national229 and international230 level was associated with IPV. However, those samples included a substantial number of older participants aged > 34 years whereas the age range of participants in the current study was restricted. Minor violence and IPV may be more common among younger men irrespective of level of deprivation.
Violence while intoxicated was more likely to occur in areas with increased deprivation. However, there was no association between substance abuse/dependence and level of deprivation that could explain the higher prevalence. Furthermore, violence occurring in pubs or bars did not demonstrate a significant association with deprivation level. There is substantial evidence that binge drinking is a strong and consistent risk factor for violence. 231 One explanation could be that binge drinking occurs more often in those exposed to higher socioeconomic deprivation, increasing the likelihood of violent incidents. Further research is necessary to investigate this hypothesis.
With increasing levels of socioeconomic deprivation victims of violence were more likely to be family members, friends and strangers, corresponding to the locations where violence occurred (own home, someone else’s home and on the streets/outdoors). The findings of a recent study suggest that there are spatiotemporal patterns of injury related to violence depending on the time of the day. 232 During the day, the locations where incidents happened and the residence of the victims were similar. During the night, however, there was a shift in the locations of violent incidents towards certain areas whereas the residence of the victims remained unchanged.
Our findings suggest that exposure to socioeconomic deprivation is associated with serious violent behaviour and that violent victimisation is not restricted to those known by the perpetrators. Although it cannot be concluded on the basis of these data that socioeconomic deprivation causes serious violence in those exposed, intervention strategies should consider contextual factors in their aim to reduce violence on the population level.
Chapter 9 Risk taking and violence
Background
The largest contributors to morbidity and mortality in adolescence are not disease and illness but behaviours such as unsafe driving, experimentation with alcohol, tobacco and illicit drugs, involvement in crime and unsafe sex. 233 Early adolescence (typically 10–14 years of age) is a critical developmental period when risk taking typically emerges. 234 However, by early adulthood many of these behaviours have receded, associated with the establishment of close emotional bonds in relationships and successful entry into the labour market. 235 However, a subgroup persist in risk-taking behaviour and this is associated with poorer physical and mental health. 236,237 Although there have been studies in developed countries of women found to be taking risks,238 substantially more men than women engage in risk taking. Among the risk-taking behaviours, violence, particularly violence involving weapons, conveys the greatest chances of infliction of serious harm. However, risk taking is also thought to involve appetitive processes in which positive rewards from the behaviour are pursued and enhanced by the risk-taking individual. 239–241 Models of risk taking therefore include the notion that people are differentially prone to take risks because of a stable, underlying difference in their risk-taking propensity. 96,234,242–245
Objectives
The objectives of this study were to:
-
investigate whether or not subgroups exist and identify a typology of risk-taking behaviour among young adult men using self-reported behaviours from the domains of substance misuse, deliberate self-harm, reckless driving, high-risk sexual behaviour and risks to long-term physical health
-
investigate the associations between subtypes of risk taking in adulthood and self-reported violence
-
investigate the associations between subtypes of risk taking in adulthood and psychiatric morbidity.
Methods
Latent class analysis can identify groups or classes in the population according to their endorsement of observed characteristics. It follows the assumption of an underlying unobserved categorical variable that separates a population into subgroups. The classes are multidimensional, as they are defined by various indicators. This approach allows description of the relationships of variables, as they combine into classes that define groups of people within a sample or population.
High-risk behaviours for violent outcomes were selected from a range of previously investigated variables from the domains of alcohol misuse, drug misuse, self-harm, reckless driving, HIV infection risk, lack of exercise and heavy drinking. 233 LCA was used to empirically define participant groups based on these risk behaviours and explore the associations of the classification scheme with violence outcomes. Decisions regarding the most appropriate model are led by statistical indicators and by conceptual considerations. We used the default estimator, which is a MLR. However, maximum likelihood may lead to the presence of a problem called local maxima. To avoid this, all LCA models were estimated with different random starting values: we used 1000 random starts at the initial stage and 100 optimisations at the final stage. Models were inspected to ensure that the log-likelihood value for each model was successfully replicated several times (an indication of low probability of local maxima).
The LCA models were then evaluated using several model fit indicators: log-likelihood, Akaike information criterion (AIC), Bayesian information criterion (BIC) and sample size adjusted BIC (aBIC). The Vuong–Lo–Mendell–Rubin likelihood ratio test (VLMR-LRT) and the entropy measure, additional important indicators of model fit, were also considered in our analysis. Higher entropy indicates overall better classification of participants into their classes.
Associations between the derived classes of multiple high risks and sociodemographic factors were estimated by fitting multinomial logistic regression models with all demographic variables entered simultaneously. Associations between the derived classes of multiple high risks and outcomes were assessed using logistic regression models, with the highest prevalence class as the reference group.
This analysis was performed using Mplus software for Windows OS version 7.11.
Results
Table 66 shows the prevalence and frequency of high-risk behaviours among young men in the population (n = 5400). The majority of men do not take health risks. The most prevalent risks included lack of exercise and substance misuse. Less than 5% of men took high risks for sexually transmitted infections (STIs).
| High-risk behaviours | No, n (%) | Yes, n (%) |
|---|---|---|
| Alcohol misuse | 4223 (83.2) | 853 (16.8) |
| Drug misuse | 4122 (82.9) | 849 (17.1) |
| Self-harm | 4752 (93.3) | 342 (6.7) |
| Suicide attempt | 4810 (93.6) | 329 (6.4) |
| Reckless driving | 4434 (88.1) | 598 (11.9) |
| STI risk | 4680 (96.8) | 156 (3.2) |
| Lack of exercise | 4260 (81.7) | 955 (18.3) |
| Heavy smoker | 4496 (85.9) | 737 (14.1) |
| None | 2122 (45.0) | – |
Table 67 shows the associations between each high-risk behaviour and any reported violence in the past 5 years. Following adjustments, all high-risk behaviours were significantly associated with violence except for lack of exercise, which decreased the risk of violence, and STI risk, which was no longer significant. Table 67 shows that there was a threefold increase in risk of violence associated with drug misuse.
| High-risk behaviours | Any violence in the last 5 years | ||
|---|---|---|---|
| n (%) | OR (95% CI) | AORa (95% CI) | |
| Alcohol misuse | 476 (29.9) | 3.34 (2.71 to 4.12)*** | 1.76 (1.38 to 2.23)*** |
| Drug misuse | 570 (36.2) | 5.94 (4.84 to 7.30)*** | 3.19 (2.49 to 4.07)*** |
| Self-harm | 210 (12.9) | 3.77 (2.84 to 4.99)*** | 1.56 (1.08 to 2.26)* |
| Suicide attempt | 209 (12.9) | 4.32 (3.20 to 5.85)*** | 1.98 (1.30 to 3.01)** |
| Reckless driving | 397 (25.2) | 5.52 (4.47 to 6.83)*** | 2.63 (2.03 to 3.42)*** |
| STI risk | 82 (5.2) | 2.24 (1.44 to 3.48)*** | 0.75 (0.48 to 1.15) |
| Lack of exercise | 282 (17.2) | 0.83 (0.69 to 0.99)* | 0.57 (0.45 to 0.71)*** |
| Heavy smoker | 346 (21.0) | 2.11 (1.76 to 2.54)*** | 1.49 (1.16 to 1.90)** |
Latent classes
We tested five models of identified subgroups defined by risk factors in the population. Model fit and information criteria for LCA model selection are provided in Table 68. All indicators of parsimony favour the models with four and five classes, with relatively similar entropy indices. Models also replicated the log-likelihood, providing evidence of the non-existence of random classes. We chose the four-class model because of its parsimony and fit indices; the five-class model was not chosen because of its lack of theoretical interpretation.
| Model | Log-likelihood | Replicated log-likelihood | AIC | BIC | aBIC | VLMR-LRT p-value | Entropy |
|---|---|---|---|---|---|---|---|
| 1C | 14178.6 | Yes | 28373.1 | 28425.8 | 28400.4 | NA | NA |
| 2C | 13118.5 | Yes | 26270.9 | 26382.9 | 26328.9 | < 0.001 | 0.742 |
| 3C | 12940.7 | Yes | 25933.3 | 26104.6 | 26022.0 | < 0.001 | 0.792 |
| 4C | 12890.3 | Yes | 25850.5 | 26081.1 | 25969.9 | 0.008 | 0.800 |
| 5C | 12841.9 | Yes | 25771.9 | 26061.8 | 25921.9 | < 0.001 | 0.786 |
Results from the LCA of the eight high-risk behaviours are shown in Table 68 and Figure 4. The classes were distributed as follows: overall low risk (class 1) 78.4%, high substance misuse (class 2) 13.6%, high self-harm (class 3) 5.6% and multiple high risks (class 4) 2.4%.
FIGURE 4.
Latent classes of multiple high-risk behaviours and substance use disorders.
The average classification probabilities for the four latent classes were 0.805 for class 1, 0.763 for class 2, 0.926 for class 3 and 0.827 for class 4.
Figure 4 shows the four latent classes identified in the population of young men aged 18–34 years. Class 1 includes more than three-quarters of the men and this group has a low prevalence of all risks except risks for future health (lack of exercise and heavy smoking), although the latter were at a much lower prevalence than in classes 2–4.
Class 2 was characterised primarily by alcohol and drug misuse and heavy smoking. In addition, men in this class took less exercise and reported a higher prevalence of reckless driving. Fewer were of South Asian or ‘other’ ethnic origin and non-UK born and more were single and unemployed than in class 1 (Table 69).
| Demographic characteristics | Violence typologies (latent classes) | |||
|---|---|---|---|---|
| Class 1,a n (%) (reference) | Class 2,a n (%) | Class 3,a n (%) | Class 4,a n (%) | |
| Age group (years) | ||||
| 18–24 (reference) | 1619 (38.5) | 285 (38.9) | 113 (37.3) | 42 (33.3) |
| 25–34 | 2587 (61.5) | 446 (61.1) | 190 (62.7) | 85 (66.7)b |
| Ethnicity | ||||
| White (reference) | 2502 (59.6) | 560 (76.6) | 239 (79.2) | 62 (92.8) |
| Black | 630 (15.0) | 97 (13.3) | 24 (8.1)c | 47 (37.0)b |
| South Asian and other | 1064 (25.4) | 74 (10.1)c | 38 (12.7)c | 18 (14.2) |
| Non-UK born | 654 (15.9) | 36 (5.0)c | 39 (12.8) | 7 (5.9)c |
| Single | 2585 (62.1) | 484 (67.2)b | 204 (67.6) | 75 (59.0) |
| Unemployed | 1522 (37.0) | 324 (46.3)b | 150 (50.7)b | 68 (53.9)b |
Class 3 was characterised primarily by high level of self-harm. In addition, men in this class were less likely to be of black and minority ethnic origin and more likely to be unemployed than men in class 1 (see Table 69).
Class 4 was characterised by multiple high risks at a higher prevalence than in the other classes. In addition, men in this class were significantly older, more likely to be black, less likely to be non-UK born and more likely to be unemployed than men in class 1 (see Table 69).
Associations between classes of risk takers and violent outcomes
Subsequent analyses of violent outcomes and class membership (see Figure 4) used the low-risk group (class 1) as a reference. The results for the main violent outcomes are shown in Tables 70 and 71. Each of the three classes of risk-taking men was significantly associated with each of the violent outcomes except for minor violence in the case of classes 3 and 4.
| Outcomes | Class membership (weighted percentages) | |||
|---|---|---|---|---|
| Class 1,a n (%) | Class 2,a n (%) | Class 3,a n (%) | Class 4,a n (%) | |
| Any violence | 951 (23.2) | 468 (64.4) | 151 (51.7) | 109 (86.6) |
| Violence while intoxicated | 322 (7.9) | 292 (42.2) | 73 (25.8) | 78 (67.1) |
| Severity of violence | ||||
| Repetitive violence | 86 (2.1) | 97 (14.3) | 29 (9.9) | 43 (36.9) |
| Victim injured | 379 (9.3) | 231 (32.0) | 66 (22.6) | 45 (36.8) |
| Perpetrator injured | 315 (7.7) | 245 (34.0) | 82 (28.3) | 70 (57.9) |
| Police involved | 178 (4.4) | 132 (18.2) | 58 (19.8) | 43 (35.0) |
| Minor violence | 267 (6.5) | 74 (10.2) | 24 (8.4) | 9 (7.2) |
| Gang fights | 78 (2.0) | 113 (16.3) | 19 (6.7) | 55 (47.1) |
| Victim of violence | ||||
| Intimate partner | 43 (1.0) | 82 (11.4) | 26 (9.0) | 50 (41.1) |
| Family member | 77 (1.9) | 93 (12.8) | 27 (9.2) | 27 (21.9) |
| Friend | 195 (4.8) | 156 (21.6) | 42 (14.3) | 44 (36.5) |
| Someone known | 259 (6.3) | 149 (20.6) | 52 (18.0) | 23 (19.2) |
| Stranger | 405 (9.9) | 217 (30.0) | 70 (24.0) | 45 (37.0) |
| Police | 47 (1.2) | 55 (7.7) | 15 (5.2) | 14 (11.3) |
| Other | 54 (1.3) | 15 (2.0) | 14 (4.9) | 9 (7.1) |
| Location of violent incident | ||||
| Own home | 76 (1.9) | 98 (13.5) | 32 (10.9) | 30 (24.3) |
| Someone else’s home | 100 (2.5) | 117 (16.2) | 30 (10.4) | 34 (27.7) |
| Outdoors/in the street | 460 (11.3) | 256 (35.5) | 88 (30.2) | 51 (42.4) |
| Bar/pub | 302 (7.4) | 222 (30.7) | 49 (16.9) | 49 (40.4) |
| Workplace | 28 (0.7) | 14 (2.0) | 9 (3.0) | 9 (7.1) |
| Sporting event | 150 (3.8) | 132 (19.0) | 23 (8.0) | 54 (46.9) |
| Outcomes | Class membership | Intraclass contrasts | |||||
|---|---|---|---|---|---|---|---|
| Class 1a (reference) | Class 2a OR (95% CI) | Class 3a OR (95% CI) | Class 4a OR (95% CI) | Class 4 – class 3 OR (95% CI) | Class 4 – class 2 OR (95% CI) | Class 3 – class 2 OR (95% CI) | |
| Any violence | Reference | 6.26 (5.06 to 7.74)*** | 3.11 (2.33 to 4.14)*** | 19.70 (11.28 to 34.39)*** | 6.34 (3.47 to 11.60)*** | 3.15 (1.78 to 5.56)*** | 0.50 (0.36 to 0.69)*** |
| Violence while intoxicated | Reference | 8.07 (6.52 to 10.00)*** | 3.09 (2.20 to 4.36)*** | 22.93 (14.57 to 36.08)*** | 7.41 (4.35 to 12.64)*** | 2.84 (1.78 to 4.52)*** | 0.38 (0.27 to 0.54)*** |
| Severity of violence | |||||||
| Repetitive violence | Reference | 7.71 (5.56 to 10.68)*** | 4.52 (2.74 to 7.46)*** | 18.82 (12.63 to 28.04)*** | 4.16 (2.43 to 7.12)*** | 2.44 (1.61 to 3.69)*** | 0.59 (0.35 to 0.98)* |
| Victim injured | Reference | 4.56 (3.68 to 5.63)*** | 2.46 (1.75 to 3.47)*** | 7.28 (4.98 to 10.63)*** | 2.96 (1.81 to 4.82)*** | 1.60 (1.07 to 2.38)* | 0.54 (0.37 to 0.78)*** |
| Perpetrator injured | Reference | 5.76 (4.50 to 7.37)*** | 4.21 (3.04 to 5.82)*** | 13.30 (9.09 to 19.47)*** | 3.16 (2.00 to 4.98)*** | 2.31 (1.54 to 3.46)*** | 0.73 (0.51 to 1.04) |
| Police involved | Reference | 4.62 (3.58 to 5.97)*** | 3.67 (2.49 to 5.41)*** | 12.86 (8.42 to 19.65)*** | 3.51 (2.07 to 5.93)*** | 2.78 (1.77 to 4.38)*** | 0.79 (0.52 to 1.20) |
| Minor violence | Reference | 1.49 (1.11 to 2.00)** | 1.26 (0.79 to 2.03) | 0.99 (0.51 to 1.90) | 0.78 (0.35 to 1.74) | 0.66 (0.33 to 1.31) | 0.85 (0.49 to 1.46) |
| Gang fights | Reference | 9.16 (6.48 to 12.96)*** | 2.91 (1.56 to 5.44)*** | 24.65 (15.97 to 38.06)*** | 8.47 (4.33 to 16.58)*** | 2.69 (1.75 to 4.13)*** | 0.32 (0.17 to 0.59)*** |
The three classes differed significantly in terms of their degree of association with violence. For any violence in the last 5 years, the multiple high-risk group (class 4) showed a significantly higher level of association than the substance misuse group (class 2). However, the substance misuse group was more likely to report violence than the self-harm group (class 3). The same differential patterns between the classes were observed for the following outcomes: violent acts when intoxicated, violence repetition, victim injured and involvement in gang fights (see Table 71).
Table 72 shows that the substance misuse (class 2) and self-harm (class 3) groups did not differ from each other in terms of the prevalence of IPV, violence towards a family member or violence towards the police. The multiple high-risk group (class 4) showed a stronger association with all victims of violence than the substance misuse group, except for someone known. The latter group had stronger associations than the self-harm group with both violence towards a friend and violence towards a stranger.
| Violence targets | Class membership | Intraclass contrasts | |||||
|---|---|---|---|---|---|---|---|
| Class 1a (reference) | Class 2a OR (95% CI) | Class 3a OR (95% CI) | Class 4a OR (95% CI) | Class 4 – class 3 OR (95% CI) | Class 4 – class 2 OR (95% CI) | Class 3 – class 2 OR (95% CI) | |
| Intimate partner | Reference | 12.32 (7.80 to 19.45)*** | 7.97 (4.29 to 14.79)*** | 37.50 (22.31 to 63.04)*** | 4.71 (2.53 to 8.75)*** | 3.04 (1.98 to 4.68)*** | 0.65 (0.37 to 1.14) |
| Family member | Reference | 7.17 (5.03 to 10.23)*** | 4.23 (2.43 to 7.37)*** | 12.73 (7.56 to 21.46)*** | 3.01 (1.56 to 5.81)** | 1.78 (1.06 to 2.97)* | 0.59 (0.34 to 1.02) |
| Friend | Reference | 4.98 (3.88 to 6.41)*** | 2.98 (2.00 to 4.43)*** | 9.98 (6.72 to 14.82)*** | 3.35 (2.03 to 5.54)*** | 2.00 (1.33 to 3.02)*** | 0.60 (0.40 to 0.90)* |
| Someone known | Reference | 3.51 (2.77 to 4.44)*** | 2.37 (1.62 to 3.47)*** | 4.39 (2.82 to 6.84)*** | 1.85 (1.07 to 3.21)* | 1.25 (0.79 to 1.98) | 0.67 (0.45 to 1.01) |
| Stranger | Reference | 3.84 (3.09 to 4.77)*** | 2.25 (1.59 to 3.17)*** | 7.74 (5.07 to 11.80)*** | 3.44 (2.10 to 5.63)*** | 2.01 (1.30 to 3.13)** | 0.59 (0.40 to 0.85)** |
| Police | Reference | 5.56 (3.68 to 8.40)*** | 2.97 (1.53 to 5.78)** | 12.57 (6.78 to 23.30)*** | 4.23 (1.91 to 9.35)*** | 2.26 (1.25 to 4.09)** | 0.53 (0.28 to 1.03) |
| Other | Reference | 1.25 (0.65 to 2.39) | 2.56 (1.23 to 5.34)* | 4.65 (2.11 to 10.23)*** | 1.81 (0.69 to 4.75) | 3.73 (1.47 to 9.49)** | 2.06 (0.86 to 4.90) |
Table 73 shows that, in most locations, violence was more prominent among classes 2 (substance misuse), 3 (self-harm) and 4 (multiple risks). All locations for violence were more prevalent among class 4 than among classes 2 and 3 (self-harm). Violence in own home, in someone else’s home, outdoors/in the street, in a bar/pub and at sporting events was more commonly reported by class 2 than by class 3.
| Location of violent incident | Class membership | Intraclass contrasts | |||||
|---|---|---|---|---|---|---|---|
| Class 1a (reference) | Class 2a OR (95% CI) | Class 3a OR (95% CI) | Class 4a OR (95% CI) | Class 4 – class 3 OR (95% CI) | Class 4 – class 2 OR (95% CI) | Class 3 – class 2 OR (95% CI) | |
| Own home | Reference | 6.50 (4.64 to 9.12)*** | 4.17 (2.47 to 7.04)*** | 13.15 (8.03 to 21.53)*** | 3.15 (1.67 to 5.98)*** | 2.02 (1.24 to 3.29)** | 0.64 (0.38 to 1.08) |
| Someone else’s home | Reference | 7.12 (5.28 to 9.61)*** | 4.20 (2.56 to 6.87)*** | 13.22 (8.12 to 21.53)*** | 3.15 (1.69 to 5.87)*** | 1.86 (1.15 to 3.00)* | 0.59 (0.36 to 0.96)* |
| Outdoors/in the street | Reference | 4.44 (3.61 to 5.47)*** | 2.79 (2.06 to 3.79)*** | 6.91 (4.74 to 10.08)*** | 2.47 (1.57 to 3.89)*** | 1.56 (1.04 to 2.32)* | 0.63 (0.45 to 0.88)** |
| Bar/pub | Reference | 5.46 (4.27 to 6.99)*** | 2.11 (1.43 to 3.10)*** | 8.19 (5.43 to 12.35)*** | 3.89 (2.31 to 6.55)*** | 1.50 (0.97 to 2.31) | 0.39 (0.26 to 0.58)*** |
| Workplace | Reference | 2.53 (1.16 to 5.52)* | 2.90 (1.04 to 8.06)* | 14.26 (6.29 to 32.30)*** | 4.92 (1.49 to 16.21)** | 5.63 (2.19 to 14.44)*** | 1.14 (0.36 to 3.63) |
| Sporting event | Reference | 6.28 (4.69 to 8.39)*** | 2.39 (1.48 to 3.87)*** | 13.74 (9.22 to 20.48)*** | 5.75 (3.30 to 10.02)*** | 2.19 (1.46 to 3.28)*** | 0.38 (0.24 to 0.62)*** |
Early adversity and childhood maltreatment
Tables 74 and 75 show the associations between class membership (see Figure 4) and reported experiences of early adversity and maltreatment before the age of 16 years. Classes 2–4 (substance misuse, self-harm and multiple risks, respectively) were each significantly more likely to report these factors than class 1 (low risk). The odds of association were higher for class 4, with significant differences from classes 2 and 3 for all childhood factors except for sexual abuse and being in local authority care compared with class 3. There were no significant differences between class 3 and class 2 except for being bullied in childhood, which was significantly higher in class 3.
| Early adversity, childhood maltreatment, mental health and criminality | Class membership (weighted percentages) | |||
|---|---|---|---|---|
| Class 1,a n (%) | Class 2,a n (%) | Class 3,a n (%) | Class 4,a n (%) | |
| Early adversity and childhood maltreatment | ||||
| Being bullied | 892 (21.2) | 252 (34.5) | 150 (49.4) | 65 (51.0) |
| Witnessed violence in the home | 267 (6.4) | 187 (25.5) | 79 (26.1) | 52 (40.8) |
| Witnessed parents fighting | 297 (7.1) | 163 (22.3) | 77 (25.5) | 34 (26.7) |
| Sexual abuse | 56 (1.3) | 33 (4.5) | 27 (8.9) | 20 (15.9) |
| Physical abuse | 139 (3.3) | 111 (15.2) | 48 (15.8) | 40 (31.3) |
| Neglect | 117 (2.8) | 77 (10.5) | 40 (13.2) | 38 (30.1) |
| Serious life-threatening injury | 59 (1.4) | 36 (4.9) | 21 (7.0) | 23 (17.8) |
| Local authority care | 81 (2.0) | 68 (9.7) | 36 (12.4) | 26 (23.5) |
| Mental health | ||||
| Anxiety | 404 (9.9) | 178 (24.7) | 132 (44.5) | 92 (73.8) |
| Depression | 385 (9.5) | 115 (15.9) | 59 (19.8) | 33 (27.5) |
| Psychosis | 46 (1.1) | 28 (3.9) | 39 (13.4) | 37 (30.1) |
| ASPD | 178 (4.5) | 331 (48.6) | 75 (27.5) | 94 (87.9) |
| Pathological gambling | 65 (1.8) | 77 (11.4) | 21 (7.5) | 68 (57.2) |
| Problem use of pornography | 22 (0.6) | 40 (5.7) | 11 (3.9) | 32 (26.4) |
| Stalking | 59 (1.4) | 44 (6.1) | 12 (3.9) | 41 (34.5) |
| Criminal history | ||||
| Conviction ever | 254 (6.4) | 257 (37.3) | 78 (27.7) | 51 (44.5) |
| Ever in prison | 74 (1.8) | 104 (14.3) | 30 (9.8) | 32 (25.4) |
| Gang membership | 9 (0.3) | 52 (8.7) | 7 (2.8) | 39 (45.3) |
| Friends encouraged crime | 186 (4.7) | 219 (32.2) | 42 (15.7) | 76 (65.3) |
| Friends encouraged drug use | 464 (11.8) | 426 (61.8) | 96 (34.6) | 92 (75.6) |
| Early adversity and child maltreatment | Class membership | Intraclass contrasts | |||||
|---|---|---|---|---|---|---|---|
| Class 1a (reference) | Class 2a OR (95% CI) | Class 3a OR (95% CI) | Class 4a OR (95% CI) | Class 4 – class 3 OR (95% CI) | Class 4 – class 2 OR (95% CI) | Class 3 – class 2 OR (95% CI) | |
| Being bullied | Reference | 1.75 (1.44 to 2.14)*** | 2.95 (2.21 to 3.94)*** | 4.77 (3.46 to 6.58)*** | 1.62 (1.08 to 2.41)* | 2.72 (1.89 to 3.91)*** | 1.68 (1.22 to 2.33)** |
| Witnessed violence in the home | Reference | 4.47 (3.54 to 5.65)*** | 3.71 (2.65 to 5.19)*** | 10.77 (7.16 to 16.19)*** | 2.90 (1.77 to 4.77)*** | 2.41 (1.57 to 3.69)*** | 0.83 (0.58 to 1.19) |
| Witnessed parents fighting | Reference | 3.45 (2.70 to 4.40)*** | 2.97 (2.12 to 4.16)*** | 6.84 (4.50 to 10.41)*** | 2.31 (1.41 to 3.78)*** | 1.98 (1.27 to 3.10)** | 0.86 (0.59 to 1.25) |
| Sexual abuse | Reference | 3.94 (2.34 to 6.64)*** | 6.85 (3.92 to 11.98)*** | 13.27 (7.44 to 23.66)*** | 1.94 (0.95 to 3.95) | 3.37 (1.76 to 6.43)*** | 1.74 (0.93 to 3.23) |
| Physical abuse | Reference | 4.71 (3.43 to 6.45)*** | 4.35 (2.91 to 6.50)*** | 11.19 (7.09 to 17.68)*** | 2.57 (1.54 to 4.31)*** | 2.38 (1.52 to 3.71)*** | 0.92 (0.62 to 1.38) |
| Neglect | Reference | 5.18 (3.69 to 7.28)*** | 4.90 (3.15 to 7.63)*** | 11.11 (6.95 to 17.76)*** | 2.27 (1.29 to 3.99)** | 2.14 (1.35 to 3.40)** | 0.95 (0.60 to 1.50) |
| Serious life-threating injury | Reference | 4.19 (2.66 to 6.60)*** | 4.43 (2.41 to 8.16)*** | 12.97 (7.55 to 22.28)*** | 2.92 (1.44 to 5.96)** | 3.10 (1.70 to 5.66)*** | 1.06 (0.58 to 1.94) |
| Local authority care | Reference | 4.67 (3.21 to 6.82)*** | 5.24 (3.09 to 8.88)*** | 9.46 (5.40 to 16.57)*** | 1.81 (0.94 to 3.46) | 2.02 (1.18 to 3.47)* | 1.12 (0.67 to 1.87) |
Psychiatric morbidity
Table 74 shows the associations between class membership (see Figure 4) and measures of psychiatric morbidity. Classes 2–4 (substance misuse, self-harm and multiple risks respectively) were all associated with a significantly higher prevalence of anxiety disorder, depression, psychosis, ASPD, pathological gambling, problem use of pornography and stalking than the low-risk group (class 1). Class 4 showed a significantly stronger association with diagnoses of anxiety disorder, ASPD, pathological gambling and stalking than both class 2 and class 3 and a significantly stronger association with psychosis than class 2. When comparing classes 3 and 2, class 3 had significantly stronger associations with anxiety disorder, depression and psychosis, whereas class 2 had a significantly stronger association with ASPD.
Criminal history
Table 74 shows the associations between class membership (see Figure 4) and measures of criminal history. All classes were significantly associated with ever having received a criminal conviction, ever having been in prison, having friends who encouraged drug use, having friends who encouraged crime and self-reported gang membership compared with class 1 (low risk).
Class 4 (multiple risks) was significantly more likely to report previous convictions, imprisonment, friends encouraging drug use and crime and gang membership than either class 3 (self-harm) or class 2 (substance abuse).
Table 74 also shows that class 2 (substance abuse) were significantly more likely to report all criminal history outcomes than class 3 (self-harm).
Adverse health and service use
Tables 76 and 77 show the associations between class membership and measures of self-reported physical and mental health problems and health service use. Compared with the low-risk group (class 1), classes 2 (substance abuse) and 4 (multiple risks) showed significant associations with all variables except for obesity.
| Health and health service use | Class membership (weighted percentages) | |||
|---|---|---|---|---|
| Class 1,a n (%) | Class 2,a n (%) | Class 3,a n (%) | Class 4,a n (%) | |
| Fair/poor health | 395 (9.5) | 139 (19.2) | 75 (25.3) | 32 (25.5) |
| Accident leading to injury | 724 (17.7) | 196 (27.8) | 111 (38.1) | 63 (51.3) |
| Accident leading to another being injured | 215 (5.3) | 70 (10.0) | 25 (8.5) | 51 (41.4) |
| Currently taking medication (physical health problem) | 229 (5.6) | 78 (11.0) | 62 (21.2) | 42 (33.4) |
| Attended A&E | 469 (12.0) | 129 (18.6) | 87 (31.7) | 58 (47.5) |
| Obesity | 420 (12.4) | 78 (13.5) | 31 (12.2) | 9 (9.5) |
| STI | 212 (5.8) | 168 (25.1) | 44 (15.6) | 69 (57.4) |
| Seen mental health professional (past 12 months) | 255 (6.2) | 87 (12.2) | 103 (35.3) | 52 (42.6) |
| Currently taking medication (mental health problem) | 93 (2.3) | 55 (7.9) | 68 (23.1) | 34 (28.0) |
| Hospitalised for psychiatric care | 89 (2.2) | 33 (4.7) | 55 (19.2) | 39 (31.7) |
| Health outcomes | Class membership | Intraclass contrasts | |||||
|---|---|---|---|---|---|---|---|
| Class 1a (reference) | Class 2a OR (95% CI) | Class 3a OR (95% CI) | Class 4a OR (95% CI) | Class 4 – class 3 OR (95% CI) | Class 4 – class 2 OR (95% CI) | Class 3 – class 2 OR (95% CI) | |
| Fair/poor health | Reference | 1.87 (1.45 to 2.40)*** | 3.16 (2.31 to 4.33)*** | 2.97 (1.93 to 4.58)*** | 0.94 (0.57 to 1.56) | 1.59 (0.99 to 2.54) | 1.69 (1.18 to 2.42)** |
| Accident leading to injury | Reference | 1.90 (1.52 to 2.39)*** | 2.69 (1.97 to 3.67)*** | 5.96 (4.11 to 8.64)*** | 2.22 (1.39 to 3.52)*** | 3.13 (2.10 to 4.67)*** | 1.41 (0.99 to 2.02) |
| Accident leading to another being injured | Reference | 2.21 (1.59 to 3.08)*** | 1.56 (0.95 to 2.54) | 9.01 (6.20 to 13.08)*** | 5.79 (3.34 to 10.03)*** | 4.07 (2.69 to 6.16)*** | 0.70 (0.41 to 1.20) |
| Current taking medication (physical health problem) | Reference | 1.68 (1.22 to 2.33)** | 4.11 (2.75 to 6.15)*** | 6.90 (4.37 to 10.90)*** | 1.68 (0.95 to 2.97) | 4.10 (2.41 to 6.97)*** | 2.44 (1.53 to 3.89)*** |
| Attended A&E | Reference | 1.46 (1.13 to 1.87)** | 3.39 (2.45 to 4.70)*** | 4.88 (3.37 to 7.05)*** | 1.44 (0.92 to 2.25) | 3.35 (2.23 to 5.03)*** | 2.33 (1.61 to 3.38)*** |
| Obesity | Reference | 1.11 (0.81 to 1.53) | 1.18 (0.76 to 1.82) | 0.55 (0.26 to 1.15) | 0.46 (0.20 to 1.06) | 0.49 (0.22 to 1.08) | 1.06 (0.64 to 1.75) |
| STI | Reference | 4.86 (3.67 to 6.43)*** | 2.83 (1.84 to 4.35)*** | 14.74 (9.76 to 22.26)*** | 5.21 (2.98 to 9.09)*** | 3.03 (1.95 to 4.71)*** | 0.58 (0.38 to 0.90)* |
| Mental health professional (12 m) | Reference | 1.74 (1.27 to 2.38)*** | 7.68 (5.53 to 10.67)*** | 9.27 (5.99 to 14.34)*** | 1.21 (0.74 to 1.97) | 5.33 (3.29 to 8.64)*** | 4.42 (2.94 to 6.62)*** |
| Current taking medication (mental health problem) | Reference | 2.75 (1.84 to 4.12)*** | 13.55 (8.98 to 20.45)*** | 11.91 (6.87 to 20.67)*** | 0.88 (0.48 to 1.60) | 4.33 (2.35 to 7.98)*** | 4.93 (3.10 to 7.83)*** |
| Hospitalised for psychiatric care | Reference | 1.88 (1.13 to 3.11)* | 10.92 (6.87 to 17.35)*** | 17.77 (10.62 to 29.75)*** | 1.63 (0.93 to 2.84) | 9.48 (5.02 to 17.90)*** | 5.82 (3.34 to 10.16)*** |
Members of class 4 were more likely to report having been involved in an accident leading to themselves and another person being injured and to have had a STI, but the other variables did not discriminate between classes 4 and 3. However, compared with class 2, members of class 4 were significantly more likely to report accidents leading to themselves and others being injured, that they were currently taking medication for both physical and mental health problems, that they had attended an A&E department, that they had had a STI and that they had consulted a professional for mental health problems in the past year or had been in a psychiatric hospital. When comparing classes 3 and 2, members of class 2 were more likely to report fair/poor physical health, that they were currently taking medication for physical health problems, that they had attended an A&E department, that they were taking medication for mental health problems, that they had consulted a health-care professional for mental health problems in the past year and that they had been in a psychiatric hospital. However, they were less likely to report that they had had a STI.
Discussion
We found that, by early adulthood, most young men are not actively engaged in taking risks with their future health or carrying out activities that result in a risk of harm to others. However, approximately one-quarter of young men in the population studied do take these risks and the risk factors are generally intercorrelated across the population. We created a typology based on three classes of risk factors. We found that class 2 was characterised primarily by substance misuse, class 3 by self-harm and class 4 by multiple high risks from all domains. Class 4 showed a higher prevalence of both substance misuse and self-harm behaviours than either class 2 or class 3. Class 4 was the smallest, but exceptionally high-risk, subgroup.
Class 4, multiple risk takers, are of particular interest and, of all subgroups of young men, place the highest burden of care on the health services. Men in class 4 were more likely to be injured in accidents, injure others, receive treatment in an A&E department, take medication for their physical health and report their health as fair/poor. They were also considerably more likely to acquire a STI and to consult a mental health professional, take psychotropic medication and report hospitalisation for psychiatric treatment. This subgroup had experienced multiple forms of childhood maltreatment and adversity and the majority has ASPD, indicating early onset of antisocial behaviour persisting into adulthood and corresponding to these experiences. The finding that members of class 4 were significantly older was unexpected. This would suggest that persistence of risk-taking behaviour had continued together with a series of criminal and violent behaviours and was associated with high levels of substance abuse.
Moffitt141 proposed a theoretical framework that makes specific predictions about risk and protective factors related to early-onset conduct disorder. Class 4 would appear to correspond to the proposed life course-persistent (or early-onset) group. This has its origins in both neurological deficits and exposure to environmental risk, such as poor parenting and parental antisocial behaviour. Neurological deficits are thought to lead to the child becoming vulnerable to poor parenting from caretakers. These early risk factors start the child on a trajectory of increasing acts and behaviours that escalate through adolescence and persist into adulthood. Our finding that persistent risk-taking is closely associated with persistent criminal and violent behaviour into the late 20s and early 30s suggests that this is a particularly severe subgroup in whom persistence of ASPD is associated with an underlying risk-taking propensity.
Previous research emphasises the importance of protective factors over the life course and the importance of accumulated adverse events over time. 246 Our study did not include protective factors but demonstrated an accumulation of early adverse events during childhood and mental health problems in adulthood that are likely to be closely inter-related and result in persistence, including anxiety and psychotic symptoms as well as substance misuse.
It has been suggested that young people who take risks are less amenable to positive protection effects as they get older if they continue to take risks. 248 For example, individuals who are still involved in violence at age 21 years are the most entrenched and committed offenders and are less amenable to change. Life-course studies of offending and associated risk taking suggest that hazard rates of these behaviours are not dynamic. 247 By sorting individuals into categories of behaviour over time, there is little evidence that the occurrence of protective factors at later time points corresponds to positive changes in behaviour for entrenched individuals. Nevertheless, employment and partner satisfaction continue to be recommended as targets for intervention when violence continues among older offenders, with confirmed effectiveness. 248
The persistence of risk taking therefore questions the purpose of these behaviours for the individual if it results in multiple adverse outcomes in adulthood, including injury, illnesses and hospitalisation. Our findings suggest an appetitive, or at least purposeful, component. This could be the result of early trauma and possible neurological deficits in these individuals. The reduced association with depression following adjustments is therefore interesting in this context. Risk taking can be construed as a displacement activity and mechanism for enhancing self-esteem in childhood, together with a means of coping with depression in adulthood to reduce the deleterious effects of a negative environment at both stages of life, including childhood maltreatment, educational failure and later unemployment and lack of a supportive emotional relationship. However, in this cross-sectional study we could not ultimately determine the direction of the association.
A longitudinal study of self-harming behaviour among adolescents shows that it resolves spontaneously in the majority of people. 249 This would indicate that class 3 represents a poor prognostic group, with self-harm persisting into adulthood. Persistence into adulthood was associated with symptoms of anxiety and depression, antisocial behaviour, high-risk alcohol use, cannabis use and cigarette smoking,249 as observed for class 3, but, in an extreme form, with multiple, other high-risk behaviours,250 as observed in class 4. In this current study, non-suicidal self-injury and suicidal behaviour were closely associated, corresponding to other studies in the community and in prisons,250–252 with additional associations observed with heavy drinking and sexual behaviours. 253 A large-scale community survey of adolescents found that those who reported non-suicidal self-injury were more depressed and hopeless, had experienced childhood physical abuse, had less parental connectedness and had run away from home. 254 Suicidal ideation and self-harm behaviour are also found to be associated with high levels of impulsivity and risk taking,255 together with high-risk use of the internet, which is closely related to symptoms of depression in studies of adolescents. 256
The distinction between classes 3 and 4 was shown in a study of self-harm among young offenders. 257 Self-harm was found to be strongly associated with a wide range of risk-taking behaviours and was considered to be a distinct epidemiological profile from that of the general population. 257 Class 4 would correspond more to this profile because it is associated with a wider range of risk-taking behaviours, higher rates of psychiatric morbidity, substance misuse and social risk factors.
The differentiation between class 2 and class 4 appears to be dependent on the level of substance misuse in the sample and its association with a wide range of other high-risk behaviours. Class 4 would appear to include men with the highest levels of substance misuse and the widest range of other behaviours, whereas class 2 appears to be restricted primarily to those with a high-level use of substances, including tobacco. A large longitudinal study of UK children followed up until 15 years of age found that smoking, alcohol use and antisocial behaviour were associated with an increased risk of morbidity and mortality. 258 At 15 years old the most prevalent behaviours were physical inactivity (74%), antisocial and criminal behaviour (42%) and hazardous drinking (34%). 258 This previous study found that boys and girls engaged in a similar number of behaviours, but that antisocial and criminal behaviours, cannabis use and vehicle-related risk behaviours (the latter observed in association with class 2 in the present study) were more prevalent among boys. Tobacco smoking, self-harm and physical inactivity were more prevalent among girls.
Finally, persistence into adulthood of substance misuse and associated risk taking may be determined by the differential development of neural circuitry and its association with both impulsive and compulsive behaviour. 259,260 This requires further investigation in the classes that we have identified (see Figure 4). The underlying biological mechanisms may underlie compulsive, impulsive and addictive behaviours and therefore may be related to the ‘appetitive’ aspects of risk-taking behaviour. 261,262
Chapter 10 Health service use and violence
Background
The importance of treatment, and access to treatment, for those with mental disorders who are violent has become increasingly recognised in recent years. This has significant implications for the successful management of violence, particularly in at-risk populations. 263,264 However, delivery of successful treatment for mental disorders is not straightforward; often those showing the highest need for treatment are also those least likely to have access to, or seek contact with, services offering such treatment. 265,266 In this chapter we consider the relationship between mental disorders, use of treatment services and violence within the UK population.
Objectives
The objectives of this chapter were to:
-
describe patterns of mental health and physical health service use in the UK household population and examine the associations of service use with violence
-
investigate whether or not mental health service use is associated with a differential risk of violence for those with a mental disorder.
Methods
Participants
For this study we analysed data on the use of services for both mental and physical health problems. We used data from the NHPMS 2000 and APMS 2007. As each of the surveys employed the same measures of demography, psychiatric morbidity, service use and violence outcomes, we conducted joint analyses of individual-level data. The total sample for the study included 15,973 men and women.
In both surveys, participants were asked a series of questions about their use of services for mental and physical health problems over the previous 2 weeks [contact with a general practitioner (GP)], quarter (inpatient or outpatient service use, use of day services), year (contact with a GP, use of medication) and lifetime (being a psychiatric inpatient).
Statistical analysis
Weighting was used to control for the under-representation of various demographic groupings in the surveys (young men, ethnic minorities, lower social classes). 45,82 The prevalence of key variables (service use and violence) was analysed in both surveys and the total sample and then the associations between, first, psychiatric morbidity and service use and, second, service use and violence were analysed using logistic regression with ORs and 95% CIs. Robust SE estimates were used to account for correlations with survey areas that may have resulted from postcode randomisation.
Throughout the analysis, adjustments were made for demographic factors and, when associations with psychiatric morbidity were explored, comorbidity. For example, analysis of psychotic illness would be adjusted for anxiety, depression, alcohol and drug misuse and personality disorder.
All analyses were performed using Stata version 13.1.
Results
Prevalence of service use
The reported use of health services for physical and mental health problems is presented in Tables 78 and 79. Table 78 shows the prevalence of different types of service use in the preceding quarter, whereas Table 79 shows the prevalence of different types of service use in the preceding 12 months. In both tables, differences between the surveys based on a chi-square test are noted after the combined prevalences; differences between men and women are noted after the individual survey findings. Significantly more matching variables between surveys were available for the last quarter outcomes. These variables were therefore used for the analysis of associations with violence; the exception was variables relating to seeing a GP, which were available for only the preceding 2 weeks. These variables relating to seeing a GP, together with the reporting of other service use in the previous quarter (see Table 78), were combined to give estimates of service use in the preceding quarter.
| Service use, last quarter | Combined sample (n = 15,983) | NHPMS 2000 | APMS 2007 | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total (n = 7393) | Men (n = 3592) | Women (n = 3801) | Total (n = 8580) | Men (n = 4285) | Women (n = 4295) | ||||||||||||
| n | % | p-value | n | % | n | % | n | % | p-value | n | % | n | % | n | % | p-value | |
| Physical health care | |||||||||||||||||
| Seen GPa | 1959 | 12.3 | ** | 988 | 11.5 | 397 | 9.3 | 591 | 13.8 | ** | 971 | 13.1 | 406 | 11.3 | 565 | 14.9 | ** |
| Inpatient stay | 444 | 2.8 | 221 | 2.6 | 106 | 2.6 | 115 | 2.7 | 223 | 3.0 | 105 | 2.9 | 118 | 3.1 | |||
| Outpatient stay | 2955 | 18.5 | 1561 | 18.2 | 725 | 16.9 | 836 | 19.5 | ** | 1394 | 18.9 | 610 | 17.0 | 784 | 20.6 | ** | |
| Day activity | 39 | 0.5 | 18 | 0.4 | 21 | 0.5 | No data | ||||||||||
| Community care | 240 | 3.0 | 115 | 2.7 | 125 | 2.9 | No data | ||||||||||
| Medication (non-psychotropic)b | 6163 | 38.6 | 3644 | 42.5 | 1253 | 29.2 | 1793 | 41.8 | ** | 3365 | 39.2 | 1357 | 31.7 | 2008 | 46.8 | ** | |
| Mental health care | |||||||||||||||||
| Seen GPa | 304 | 1.9 | ** | 136 | 1.6 | 49 | 1.0 | 86 | 2.0 | ** | 168 | 2.3 | 65 | 1.8 | 103 | 2.7 | * |
| Inpatient stay | 14 | 0.1 | 9 | 0.1 | 5 | 0.1 | 3 | 0.1 | 6 | 0.1 | 5 | 0.1 | 1 | 0.1 | |||
| Outpatient stay | 124 | 0.8 | 59 | 0.7 | 27 | 0.6 | 32 | 0.7 | 65 | 0.9 | 37 | 1.0 | 29 | 0.8 | |||
| Day activity | 6 | 0.1 | 2 | 0.1 | 4 | 0.1 | No data | ||||||||||
| Community care | 39 | 0.5 | 20 | 0.5 | 19 | 0.5 | No data | ||||||||||
| Psychotropic medicationb | 968 | 6.1 | 509 | 5.9 | 169 | 3.9 | 340 | 7.9 | ** | 459 | 6.2 | 148 | 4.1 | 311 | 8.2 | ** | |
| Service use, last 12 months | Combined sample (n = 15,983) | NHPMS 2000 | APMS 2007 | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total (n = 7393) | Men (n = 3592) | Women (n = 3801) | Total (n = 8580) | Men (n = 4285) | Women (n = 4295) | ||||||||||||
| n | % | p-value | n | % | n | % | n | % | p-value | n | % | n | % | n | % | p-value | |
| Physical health care | |||||||||||||||||
| Seen GP | 9761 | 61.1 | 5204 | 60.7 | 2445 | 57.1 | 2758 | 64.2 | ** | 4557 | 61.6 | 2068 | 57.6 | 2489 | 65.5 | ** | |
| Inpatient stay | No data | 767 | 10.4 | 328 | 9.1 | 439 | 11.6 | ** | |||||||||
| Community/outpatient | No data | No data | |||||||||||||||
| Mental health care | |||||||||||||||||
| Seen GP | 1823 | 11.4 | 1005 | 11.7 | 341 | 8.0 | 665 | 15.5 | ** | 817 | 11.1 | 255 | 7.1 | 562 | 14.8 | ** | |
| Inpatient staya | 373 | 2.3 | 210 | 2.5 | 96 | 2.2 | 114 | 2.7 | 163 | 2.2 | 69 | 1.9 | 94 | 2.5 | |||
| Community/outpatient | No data | 490 | 6.6 | 210 | 5.9 | 280 | 7.4 | * | |||||||||
A significantly greater proportion of individuals reported going to see their GP for physical health issues in the preceding 2 weeks in the 2007 sample than in the 2000 sample (OR 1.16, 95% CI 1.04 to 1.29; p = 0.004). The proportion of individuals seeing their GP for mental or emotional difficulties in the preceding 2 weeks was also greater in the 2007 sample (OR 1.45, 95% CI 1.15 to 1.83; p = 0.002). However, this finding was not replicated when participants were asked about health service use in the previous year as a whole. All other service use variables showed a comparable prevalence between the surveys, when matching data were available.
Table 80 shows the associations between clinical and demographic factors and service use in the preceding quarter. In a fully adjusted regression model, only age, sex, anxiety disorder and depression were significantly linked with physical service use. Being female (AOR 1.71, 95% CI 1.58 to 1.85; p < 0.001) and older were associated with increased service use after adjustment, as were anxiety (AOR 1.78, 95% CI 1.59 to 2.00; p < 0.001) and depression (AOR 1.45, 95% CI 1.05 to 1.99; p = 0.023). ASPD also showed a trend towards significance at the α = 0.05 level.
| Covariate | Physical health service use (last quarter) | Mental health service use (last quarter) | ||||||
|---|---|---|---|---|---|---|---|---|
| n (%) | AORa | 95% CI | p-value | n (%) | AORa | 95% CI | p-value | |
| Sex | ||||||||
| Female | 4419 (54.6) | Reference | 772 (9.5) | Reference | ||||
| Male | 3250 (41.3) | 0.60 | 0.55 to 0.64 | < 0.001 | 394 (5.0) | 0.51 | 0.43 to 0.59 | < 0.001 |
| Age group (years) | ||||||||
| 16–34 | 1881 (35.5) | Reference | 256 (4.8) | Reference | ||||
| 35–54 | 2618 (43.8) | 1.37 | 1.24 to 1.42 | < 0.001 | 516 (8.6) | 1.78 | 1.39 to 2.28 | < 0.001 |
| ≥ 55 | 3432 (73.0) | 5.13 | 4.59 to 5.74 | < 0.001 | 393 (8.4) | 2.18 | 1.21 to 1.77 | < 0.001 |
| Marital status | ||||||||
| Married/cohabiting | 5517 (53.6) | Reference | 734 (7.1) | Reference | ||||
| Single | 1607 (38.2) | 1.02 | 0.92 to 1.14 | 0.701 | 226 (5.4) | 0.85 | 0.67 to 1.09 | 0.205 |
| Divorced/separated | 807 (54.7) | 1.04 | 0.92 to 1.16 | 0.554 | 206 (14.0) | 1.50 | 1.25 to 1.80 | < 0.001 |
| Social class | ||||||||
| I and II | 2596 (47.7) | Reference | 304 (5.6) | Reference | ||||
| IIIM and IIINM | 3247 (50.9) | 1.07 | 0.98 to 1.17 | 0.131 | 482 (7.6) | 1.29 | 1.09 to 1.52 | 0.004 |
| IV, V and VI | 1708 (52.4) | 1.08 | 0.97 to 1.20 | 0.171 | 305 (9.4) | 1.46 | 1.21 to 1.77 | < 0.001 |
| Ethnicity | ||||||||
| White | 7356 (50.5) | Reference | 1088 (7.5) | Reference | ||||
| Black | 173 (41.8) | 0.88 | 0.68 to 1.14 | 0.344 | 27 (6.5) | 0.67 | 0.40 to 1.11 | 0.117 |
| South Asian | 178 (34.7) | 0.78 | 0.59 to 1.03 | 0.076 | 18 (3.6) | 0.30 | 0.12 to 0.75 | 0.010 |
| Other | 167 (45.5) | 1.23 | 0.92 to 1.66 | 0.166 | 15 (4.0) | 0.53 | 0.27 to 1.05 | 0.068 |
| Drug dependency | 206 (35.9) | 0.85 | 0.66 to 1.10 | 0.224 | 81 (14.1) | 2.34 | 1.57 to 3.50 | < 0.001 |
| Alcohol dependency | 302 (40.6) | 0.95 | 0.77 to 1.18 | 0.654 | 84 (11.2) | 1.53 | 1.07 to 2.19 | 0.02 |
| Anxiety disorder | 1397 (59.9) | 1.78 | 1.59 to 2.00 | < 0.001 | 443 (23.7) | 4.75 | 4.07 to 5.55 | < 0.001 |
| Depression | 168 (59.6) | 1.45 | 1.05 to 1.99 | 0.023 | 151 (53.7) | 8.09 | 5.62 to 11.62 | < 0.001 |
| Psychosis | 23 (70.0) | 1.62 | 0.70 to 3.76 | 0.262 | 28 (82.9) | 51.2 | 15.3 to 171.8 | < 0.001 |
| ASPD | 164 (46.7) | 1.32 | 0.99 to 1.75 | 0.056 | 39 (11.3) | 0.98 | 0.62 to 1.56 | 0.94 |
Patterns of association with mental health service use appeared more multifactorial than for physical service use. Female sex and older age were still significant predictors of mental health service use, but so too was being divorced or separated and being in social classes III or IV–VI. Additionally, being of South Asian ethnicity was negatively associated with use of mental health services after adjustment for other demographic factors and the presence of mental disorder. As expected, the presence of all mental disorders was positively associated with the use of mental health services, except for ASPD, which showed no association after adjustment.
Associations with violence
Associations with violent behaviour of those using health services in the previous quarter are presented in Table 81. The associations are presented first unadjusted and then adjusted for possible confounding variables based on the analysis above.
| Outcomes | Physical health service use | Mental health service use | ||||
|---|---|---|---|---|---|---|
| n (%) | OR (95% CI) | AORa (95% CI) | n (%) | OR (95% CI) | AORb (95% CI) | |
| Any violence | 591 (7.6) | 0.56 (0.50 to 0.64)*** | 1.08 (0.93 to 1.25) | 148 (13.0) | 1.36 (1.11 to 1.66)** | 1.56 (1.18 to 2.05)** |
| Violence while intoxicated | 214 (2.7) | 0.45 (0.37 to 0.55)*** | 0.88 (0.71 to 1.10) | 59 (5.2) | 1.22 (0.90 to 1.65) | 1.27 (0.80 to 2.02) |
| Severity of violence | ||||||
| Minor violence | 229 (2.9) | 0.53 (0.43 to 0.66)*** | 0.97 (0.77 to 1.21) | 58 (5.1) | 1.27 (0.2 to 1.75) | 1.88 (1.27 to 2.79)** |
| Five or more incidents | 131 (1.7) | 0.65 (0.49 to 0.86)** | 1.34 (0.99 to 1.81) | 32 (2.8) | 1.38 (0.92 to 2.09) | 1.36 (0.81 to 2.29) |
| Victim injured | 191 (2.5) | 0.61 (0.49 to 0.76)*** | 1.23 (0.96 to 1.57) | 44 (3.9) | 1.23 (0.86 to 1.77) | 1.06 (0.65 to 1.72) |
| Perpetrator injured | 211 (2.7) | 0.80 (0.56 to 0.87)** | 1.29 (1.02 to 1.64)* | 52 (4.5) | 1.46 (1.07 to 1.99)* | 1.20 (0.79 to 1.83) |
| Police involved | 172 (2.2) | 0.67 (0.53 to 0.84)** | 1.18 (0.92 to 1.52) | 50 (4.4) | 1.73 (1.25 to 2.40)** | 1.55 (1.02 to 2.34)* |
| Victim of violence | ||||||
| Intimate partner | 142 (1.8) | 1.32 (0.99 to 1.74) | 1.60 (1.19 to 2.14)** | 56 (4.9) | 3.76 (2.74 to 5.16)*** | 2.13 (1.46 to 3.11)*** |
| Family | 71 (0.9) | 0.87 (0.58 to 1.30) | 1.41 (0.93 to 2.14) | 13 (1.2) | 1.23 (0.67 to 2.23) | 0.63 (0.30 to 1.33) |
| Friend | 88 (1.2) | 0.40 (0.29 to 0.55)*** | 0.82 (0.59 to 1.12) | 26 (2.2) | 1.15 (0.72 to 1.83) | 1.38 (0.70 to 2.73) |
| Person known | 190 (2.4) | 0.59 (0.47 to 0.75)*** | 1.16 (0.89 to 1.50) | 47 (4.1) | 1.30 (0.90 to 1.87) | 1.25 (0.77 to 2.03) |
| Stranger | 270 (3.5) | 0.62 (0.43 to 0.62)*** | 1.09 (0.88 to 1.34) | 56 (4.9) | 0.99 (0.72 to 1.36) | 1.09 (0.71 to 1.68) |
| Police | 35 (0.5) | 0.68 (0.41 to 1.13) | 1.34 (0.78 to 2.34) | 14 (1.2) | 2.44 (1.29 to 4.64)** | 1.75 (0.77 to 3.99) |
| Other | 36 (0.5) | 0.50 (0.31 to 0.82)** | 0.96 (0.57 to 1.62) | 6 (0.6) | 0.81 (0.37 to 1.78) | 0.84 (0.31 to 2.29) |
| Location of violent incident | ||||||
| Own home | 161 (2.1) | 1.27 (0.97 to 1.65) | 1.60 (1.21 to 2.12)** | 62 (5.5) | 3.63 (2.69 to 4.90)*** | 2.04 (1.42 to 2.95)*** |
| Someone else’s home | 53 (0.7) | 0.63 (0.40 to 0.97)* | 1.05 (0.68 to 1.63) | 19 (1.6) | 2.02 (1.19 to 3.42)** | 1.78 (0.96 to 3.30) |
| Street | 317 (4.1) | 0.52 (0.44 to 0.62)*** | 1.02 (0.84 to 1.24) | 87 (7.6) | 1.38 (1.06 to 1.79)* | 1.48 (1.03 to 2.13)* |
| Bar/pub | 191 (2.5) | 0.55 (0.44 to 0.68)*** | 1.09 (0.86 to 1.37) | 47 (4.1) | 1.21 (0.85 to 1.73) | 1.19 (0.73 to 1.94) |
| Workplace | 39 (0.5) | 0.63 (0.39 to 1.03) | 1.13 (0.68 to 1.89) | 10 (0.9) | 1.37 (0.57 to 3.33) | 1.50 (0.46 to 4.89) |
| Other | 88 (1.3) | 0.58 (0.40 to 0.84)** | 1.26 (0.85 to 1.87) | 15 (1.3) | 0.83 (0.46 to 1.50) | 1.03 (0.50 to 2.15) |
Service use for physical health problems showed many significant associations with violence. However, following adjustment, significant associations were found for violence involving injury to the perpetrator, IPV and violence in the perpetrator’s home.
Use of services for mental or emotional health was positively associated with violence in this analysis following adjustment. It was not linked to violence when intoxicated nor to any form of severe violence (i.e. repeated violence or violence resulting in injury). However, it was associated with minor violence that did not lead to injury to any party, although the police were more likely to be involved. Mental health service users were also more than twice as likely as those not using services to be involved in IPV and violence in the home and were also more likely to be involved in street violence.
Service use compared with non-service use
A final analysis was conducted to differentiate patterns of violence between those individuals with mental disorder who made use of services and those who screened positive for one or more mental disorder (anxiety disorder, alcohol or drug dependence, psychosis or ASPD) but who did not use services for mental or emotional problems.
A total of 3325 individuals (20.8% of the total sample) screened positive for one or more mental disorders. Of these, only 631 (19.0%) reported any service use within the last quarter, meaning that 2695 individuals (16.9% of the total sample) met caseness criteria for a mental disorder but had not recently accessed services. This group was defined as having an ‘unmet need’ for service use. The two groups (service use and unmet need) were compared on demography, prevalence of mental disorder and violence. Those in the unmet need group were more likely to be male (AOR 3.51, 95% CI 2.58 to 4.79; p < 0.001) and younger in age and less likely to be divorced or separated (AOR 0.67, 95% CI 0.56 to 0.83; p < 0.001); they were considerably more likely to be of South Asian origin (AOR 6.51, 95% CI 1.40 to 30.4; p = 0.017) and less likely to be of social classes III (AOR 0.75, 95% CI 0.58 to 0.97; p = 0.029) or IV–VI (AOR 0.62, 95% CI 0.46 to 0.82; p = 0.001); and they were also more likely to suffer from a higher number of mental disorders (Wilcoxon z = 2.17, p = 0.030) and to have been involved in violence in the past 5 years (AOR 1.39, 95% CI 1.09 to 1.77; p = 0.009).
The ORs of associations with violence for those who used services and those who did not are presented in Table 82. The analysis was adjusted for demography and all other mental disorders. Within this sample, no individual who screened positive for the presence of a psychotic disorder had not sought treatment within the last quarter and so the odds of violence in this group could not be calculated.
| Mental disorder | Used services, AORa (95% CI) | No service use, AORa (95% CI) |
|---|---|---|
| Drug dependence | 2.89 (1.37 to 6.14)** | 1.56 (1.09 to 2.24)* |
| Psychosis | 0.58 (1.62 to 2.11) | – |
| Alcohol dependence | 2.57 (1.31 to 5.04)** | 1.79 (1.27 to 2.52)** |
| Anxiety | 0.73 (0.36 to 1.45) | 1.03 (0.73 to 1.45) |
| ASPD | 3.60 (1.37 to 9.42)** | 2.31 (1.57 to 3.40)*** |
Anxiety disorder showed no independent association with violence for either group, but for all other mental disorders the odds for involvement with violence were lower in the group who did not use services.
Discussion
This chapter considers associations between making use of health services and involvement in violence. A distinction was made between use of services for physical health problems and use of services for mental health problems and a further analysis was conducted to explore the effect of receiving treatment compared with not receiving treatment for those with a mental disorder.
Service use for physical health problems was not associated with violence generally and was found to be protective of most forms of severe violence before adjustment. Violence requires significant physical resources and those seeking treatment for physical problems may find themselves limited in opportunities or physical ability to be violent. Indeed, the only form of serious violence directly associated with use of physical health services after adjustment was violence resulting in injury to the perpetrator (i.e. the respondent). Logically, those who are injured as a result of violence – regardless of who initiated the violence – will make use of physical health services; no such finding was observed for mental health service use following injury.
Although physical service use was not associated with violence, of those who were violent, those receiving physical services were more likely than those not using services to have been involved in violence against a partner and to be violent in the home. These two findings are likely to be related, given that IPV most commonly takes place at home and is itself one of the most common forms of violence in the UK, accounting for between 16% and 25% of all violent crime. 267 The association with physical health service use may be spurious or it may be related to a link between physical ill health and confinement to the home setting, increasing domestic disharmony. However, it should be understood in the context of there being no overall association between service use for physical health and violence.
Use of health services for mental and emotional difficulties was positively associated with involvement in violence in the preceding 5 years. This finding, although interesting and consistent with the results of previous research,264,265 is harder to interpret given the differing reporting periods of the outcome variables, with service use measured over the previous quarter and violence over the previous 5 years. It is not possible to say whether the violence was committed as a result of mental disorder or vice versa. It is possible that an incident of violence led an individual to seek treatment for a mental disorder when previously he or she may not have done so.
Finally, although not using mental health services was associated with higher odds of violence than using mental health services, associations between mental disorder and violence were stronger in the group who used services than in the group who did not. This suggests that risk factors other than mental disorder are the major drivers of violence in those who do not use mental health services. This is perhaps to be expected from the demographic composition of the group within this sample, which tended to consist of younger men who would naturally be at a higher risk of violence on the evidence of most previous studies of criminality and violence. Alternatively, there may be a role for comorbidity within the propensity for individuals to use services and/or commit violence.
Chapter 11 Impact of violence on health-care costs
Background
It is likely that violence will result in high costs in relation to the victim. However, it is unclear whether or not violent behaviour in the general population places a heavy burden on health and social services in relation to the perpetrator.
Objective
The objective of this study was to measure and compare the service use and costs for three specific groups: (1) a representative sample of the population, (2) those who have committed acts of violence and (3) those who have not committed acts of violence.
Methods
Analyses were conducted to assess the impact of violent behaviour on resource use. The perspective was that of the health-care system, with the addition of some social care services. The analyses proceeded in two stages. First, an estimate was made of the overall service use and costs over a retrospective period for members of the survey. Second, we examined the impact of violence on service costs. This was performed using univariate and multivariate analyses.
Resource use
Resource use data were based on information collected as part of the NHPMS 2000. This yielded a total sample of 8580 participants. Details have been reported previously. 45 In addition to demographic and clinical data, the survey collected information on a range of health-care services that participants used over 1 year and 3 months preceding the date of the survey. The service use information collected included the numbers of contacts with GPs, days spent in hospital, outpatient visits, day care attendances and contacts with community-based professionals such as community psychologists, community psychiatrists, community psychiatric nurses and social workers.
Data were collected for different time periods but for these analyses and for the purposes of comparability resource use data recorded for the 3 months preceding the survey date were used. When a service had been used but the quantity or the contact duration was missing, the median values from those who provided this information were used. In addition, when no information was collected (as for the number of outpatient visits for general health), an external source was used to estimate the number of contacts.
Unit costs
All unit costs, in UK pounds sterling, were estimated at 2012–13 prices and collected from sources in the public domain. The unit costs are summarised in Table 83. Costs per unit of measurement for each type of service (such as GP visits, inpatient days, outpatient attendances) were taken from Curtis. 268 The NHS Schedule of Reference Costs was used to estimate the costs of psychiatric inpatient days and outpatient attendances (in addition to Curtis268). Unit costs for orthopaedic physicians and osteopaths were taken from the Bupa website [www.bupa.co.uk (accessed 16 February 2016)] and the General Osteopathic Council website [www.osteopathy.org.uk (accessed 16 February 2016)] respectively. Unit costs for some services (such as self-help/support groups) were not identified and other services were used as proxies for these (see Table 83).
| Service | Unit cost (£) | Source | Comments |
|---|---|---|---|
| GP: psychiatric and non-psychiatric | |||
| GP (per surgery consultation) | 34 | Curtis268 | |
| Inpatient: non-psychiatric | |||
| Inpatient (bed-days) | 598 | Curtis268 | |
| Inpatient: psychiatric | |||
| Acute psychiatric ward (bed-days) | 430 | Curtis268 | |
| A&E (care contact) | 204 | NHS reference costs 2012–13269 | |
| Rehabilitation ward (bed-days) | 595 | NHS reference costs 2012–13269 | |
| General medical ward (bed-days) | 430 | Curtis268 | |
| Outpatient: non-psychiatric | |||
| Outpatient (appointment) | 135 | Curtis268 | |
| Outpatient: psychiatric | |||
| A&E (care contact) | 204 | NHS reference costs 2012–13269 | |
| Psychiatric outpatient department (attendance) | 100 | Curtis268 | |
| Hospital outpatient department (attendance) | 100 | Curtis268 | |
| Alcohol clinic (attendance) | 104 | NHS reference costs 2012–13269 | |
| Bupa outpatient (attendance) | 100 | Curtis268 | Unit cost not found. PSSRU 2013 unit cost for the weighted average of all adult outpatient attendances for mental health services was used as a proxy |
| Psychotherapist (attendance) | 100 | Curtis268 | Unit cost not found. PSSRU 2013 unit cost for the weighted average of all adult outpatient attendances for mental health services was used as a proxy |
| Alternative therapy centre (attendance) | 100 | Curtis268 | Unit cost not found. PSSRU 2013 unit cost for the weighted average of all adult outpatient attendances for mental health services was used as a proxy |
| Physiotherapist (attendance) | 50 | NHS reference costs 2012–13 | |
| Day services: psychiatric | |||
| Community mental health centre (hour) | 36 | Curtis268 | |
| Day activity centre (session) | 38 | Curtis268 | |
| Sheltered workshop (hour) | 11 | Curtis268 | Taken from PSSRU 2009/10 (£9.80).270 Cost inflated to 2013 unit cost (£11) |
| Cardiac rehabilitation (appointment) | 265 | NHS reference costs 2012–13269 | |
| Community care: psychiatric | |||
| Psychiatrist (contact) | 145 | NHS reference costs 2012–13269 | Unit cost not found. NHS reference costs 2012–13269 unit cost for other specialist mental health services was used as a proxy |
| Psychologist (contact) | 134 | Curtis268 | |
| Community psychiatric nurse (contact) | 65 | Curtis268 | |
| Social worker (contact) | 159 | Curtis268 | |
| Self-help/support group (session) | 30 | Curtis268 | Unit cost not found. PSSRU 2013 unit cost for private sector day care for people with mental health problems was used as proxy |
| Home help/home-care worker (contact) | 24 | Curtis268 | |
| Outreach worker/family support (hour) | 49 | Curtis268 | |
| Community chiropodist (hour) | 30 | Curtis268 | |
| Community midwife (contact) | 68 | NHS reference costs 2012–13269 | |
| Community physiotherapist (hour) | 30 | Curtis268 | |
| Community speech and language therapist (hour) | 30 | Curtis268 | |
| District nurse (hour) | 60 | Curtis268 | |
| Health visitor (hour) | 61 | Curtis268 | |
| Community occupational therapist (hour) | 30 | Curtis268 | |
| General practice nurse | 44 | Curtis268 | |
| Hospital psychiatric nurse (hour) | 84 | Curtis268 | |
| Orthopaedic physician (session) | 75 | www.bupa.co.uk | |
| Osteopath (session) | 50 | www.osteopathy.org.uk | |
| Macmillan nurse (contact) | 60 | NHS reference costs 2012–13269 | Unit cost not found. NHS reference costs 2012–13269 unit cost for other specialist nursing was used as a proxy |
| Parentcraft (session) | 83 | NHS reference costs 2012–13269 | |
| Support group (session) | 30 | Curtis268 | |
| School-based nurse (contact) | 46 | NHS reference costs 2012–13269 | |
| Rheumatology nurse (contact) | 45 | NHS reference costs 2012–13269 | Unit cost not found. NHS reference costs 2012–13269 unit cost for arthritis nursing was used as a proxy |
| Asthma and respiratory nurse (contact) | 75 | NHS reference costs 2012–13269 | |
| Continence nurse (contact) | 85 | NHS reference costs 2012–13269 | |
| Diabetic nurse (contact) | 70 | NHS reference costs 2012–13269 | |
| Stoma nurse (contact) | 43 | NHS reference costs 2012–13269 | |
Cost calculations
Costs were categorised into five groups: GP costs, inpatient costs, outpatient costs, day services costs and community care costs. Each category was further grouped into psychiatric and non-psychiatric costs. The definition of ‘psychiatric’, taken from the survey, was any service use related to a mental health problem as well as a combination of a mental health and a physical health complaint. ‘Non-psychiatric’ was defined as any service use related to a physical complaint only. The reason for this grouping of service costs was to see whether or not violence was more likely to affect psychiatric care costs than non-psychiatric care costs.
Service costs were derived by combining service use with the appropriate national unit costs. Costs were calculated for the period of 3 months preceding the survey date for all services except for GP care for which the cost was extrapolated from 2 weeks preceding the survey date to 3 months preceding the survey date.
Analyses
Service use over the 3-month period was described by the numbers of participants with and without contact with each service sector, the mean numbers of contacts for those who used these services and the mean cost for each participant. These data were presented for those with self-reported violence and those with no self-reported violence.
The impact of violence on service costs was estimated using ordinary least squares regression. Separate models were run for each service category and each measure of violence adjusting for age, sex, social class, marital status and employment. We also separately report models adjusting for psychiatric comorbidity. Given the non-normality of the cost distribution, the CIs around the regression coefficients were generated using non-parametric bootstrap methods. This involved random sampling with replacement 1000 times from the original data set and generating percentile CIs from these 1000 samples. 271 A significance level of 5% was used and statistical analyses were performed using Stata version 12.
Results
Service use
The numbers of participants using services at least once over the 3 months prior to the survey date are provided in Table 84. Of the overall sample, 18.9% were in contact with non-psychiatric outpatient services. A similar contact rate was observed for the ‘any violence’ and ‘no violence’ samples. Those reporting violence were more likely to have had a psychiatric inpatient admission, although this was still rare. Most of the other differences between the groups were small.
| Service sector | n (%) with contact | n (%) no contact | |
|---|---|---|---|
| Psychiatric | Non-psychiatric | ||
| GPa | |||
| Total sample | 156 (1.8) | 979 (11.4) | 7445 (86.8) |
| Any violence | 25 (3.1) | 72 (9.0) | 707 (87.9) |
| No violence | 127 (1.7) | 870 (11.5) | 6566 (86.8) |
| Inpatient | |||
| Total sample | 11 (0.1) | 248 (2.9) | 8231 (95.9) |
| Any violence | 4 (0.5) | 25 (3.1) | 775 (96.4) |
| No violence | 6 (0.08) | 213 (2.8) | 7344 (97.1) |
| Outpatient | |||
| Total sample | 71 (0.8) | 1624 (18.9) | 6885 (80.2) |
| Any violence | 13 (1.6) | 145 (18.0) | 646 (80.4) |
| No violence | 50 (0.66) | 1431 (18.9) | 6082 (80.4) |
| Day activity | |||
| Total sample | 52 (0.6) | 0 | 8528 (99.3) |
| Any violence | 7 (0.9) | 0 | 797 (99.1) |
| No violence | 37 (0.5) | 0 | 7526 (99.5) |
| Community care | |||
| Total sample | 332 (3.9) | 0 | 8248 (96.1) |
| Any violence | 45 (5.6) | 0 | 759 (94.4) |
| No violence | 270 (3.6) | 0 | 7293 (96.4) |
Table 85 details the intensity of service use by participants. The data in the table relate only to those who actually use the service, that is, zero values are omitted. Psychiatric services were used more often than non-psychiatric services. The mean numbers of contacts with GPs and days in hospital for psychiatric reasons were higher for participants who reported violent behaviour than for those reporting no violence. In addition, participants who reported violent behaviour had fewer contacts with outpatient, day activity and community care services for psychiatric reasons (2.6, 3.9 and 6.2 respectively) than the overall sample (3.4, 15.4 and 12.9 respectively) and those reporting no violence (3.8, 17.2 and 12.5 respectively).
| Service sector | Mean (SD) number of contacts | |
|---|---|---|
| Psychiatric | Non-psychiatric | |
| GPa | ||
| Total sample | 8.5 (4.6) | 7.8 (3.9) |
| Any violence | 9.8 (7.2) | 9.1 (5.2) |
| No violence | 8.5 (4.6) | 7.8 (3.9) |
| Inpatient | ||
| Total sample | 13.8 (26.7) | 6.5 (11.3) |
| Any violence | 18.8 (39.8) | 3.9 (5.9) |
| No violence | 11.7 (15.5) | 6.6 (11.8) |
| Outpatient | ||
| Total sample | 3.4 (4.0) | 1.28 (1.59)b |
| Any violence | 2.6 (2.9) | 1.28 (1.59)b |
| No violence | 3.8 (4.5) | 1.28 (1.59)b |
| Day activity | ||
| Total sample | 15.4 (15.8) | – |
| Any violence | 3.9 (1.9) | – |
| No violence | 17.2 (15.8) | – |
| Community care | ||
| Total sample | 12.9 (24.6) | – |
| Any violence | 6.2 (5.8) | – |
| No violence | 12.5 (24.8) | – |
Total costs
Table 86 provides details of the costs by service group. The mean costs in the table relate to the whole sample, that is, with zero values included. The mean cost for GP contacts was highest if the contacts were for non-psychiatric reasons. A similar result was also observed for inpatient and outpatient services.
| Service sector | Mean (SD) cost (£) of service | |
|---|---|---|
| Psychiatric | Non-psychiatric | |
| GPa | ||
| Total sample | 4.6 (39.1) | 31.2 (96.9) |
| Any violence | 7.7 (51.3) | 28.4 (104.3) |
| No violence | 4.4 (37.5) | 31.1 (96.1) |
| Inpatient | ||
| Total sample | 10.6 (642.2) | 112.1 (1321.7) |
| Any violence | 68.4 (1888.7) | 72.1 (732.0) |
| No violence | 4.7 (297.7) | 111.4 (1354.9) |
| Outpatient | ||
| Total sample | 3.1 (49.0) | 40.2 (92.2) |
| Any violence | 4.9 (51.1) | 38.5 (90.7) |
| No violence | 2.8 (49.0) | 40.3 (92.5) |
| Day activity | ||
| Total sample | 13.7 (294.1) | 0 |
| Any violence | 2.4 (37.2) | 0 |
| No violence | 11.7 (255.3) | 0 |
| Community care | ||
| Total sample | 32.4 (582.0) | 0 |
| Any violence | 38.1 (388.1) | 0 |
| No violence | 26.3 (509.2) | 0 |
For all service groups, when the contact was for psychiatric reasons, with the exception of day activities, the mean cost of care was higher for those reporting violent behaviour than for those who did not report violent behaviour. The opposite was the case for services use for non-psychiatric reasons.
The total cost (i.e. the summation of all individual service costs) was not separated into psychiatric compared with non-psychiatric services (as some are explicitly psychiatric). The mean cost of all services was £217.65 (SD £1677.21) for the total sample, £229.88 (SD £2072.29) for those reporting any violence and £202.70 (SD £1529.63) for those not reporting any violence.
Regression analysis
The results of the regression analysis are reported in Table 87 (adjusted for sex, age, social class, marital status, employment status and psychiatric comorbidity) and Table 88 (adjusted for sex, age, social class, marital status and employment status only).
| Covariate | Observed (95% CI); SE (£) | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Total costs | GP non-psychiatric cost | GP psychiatric cost | Inpatient non-psychiatric cost | Inpatient psychiatric cost | Outpatient non-psychiatric cost | Outpatient psychiatric cost | Day care services cost | Community care cost | |
| Any violence | 48.63 (–86.37 to 232.01); 85.65 | 0.36 (–0.95 to 1.80); 0.71 | 0.04 (–0.48 to 0.64); 0.29 | 5.39 (–52.85 to 79.37); 33.69 | 72.15 (–14.21 to 249.87); 81.16 | 6.23 (–1.08 to 13.81); 3.85 | 0.25 (–4.05 to 4.77); 2.23 | –21.88 (–39.07 to –8.21); 7.81 | 13.91 (–56.79 to 25.53); 21.17 |
| Violent while intoxicated | 141.15 (–130.33 to 583.44); 202.38 | 0.51 (–1.40 to 3.20); 1.11 | 0.41 (–0.54 to 1.42); 0.52 | 7.53 (–53.96 to 91.16); 38.06 | 187.00 (–13.82 to 637.27); 199.12 | 3.35 (–7.55 to 15.42); 5.9 | –0.16 (–5.22 to 5.02); 2.6 | –23.02 (–49.36 to 2.44); 12.29 | –34.47 (–96.82 to 18.80); 29.83 |
| Five or more violent incidents | 311.31 (–93.61 to 1010.07); 305.61 | 2.66 (–0.58 to 6.26); 1.80 | 0.15 (–1.03 to 1.69); 0.68 | 17.15 (–42.87 to 101.94); 38.40 | 305.54 (–18.01 to 1073.59); 319.68 | 12.95 (–1.50 to 28.38); 7.6 | –4.30 (–11.37 to 2.06); 3.45 | –28.36 (–49.96 to –10.81); 10.35 | 5.52 (–73.80 to 119.45); 49.63 |
| Minor violence | –27.27 (–130.40 to 120.31); 63.97 | –0.61 (–1.90 to 0.86); 0.72 | 0.13 (–0.59 to 0.96); 0.40 | 40.20 (–53.92 to 166.99); 58.87 | –16.09 (–42.69 to –1.46); 11.24 | –6.62 (–14.57 to 0.94); 3.88 | –1.81 (–5.19 to 1.27); 1.63 | –15.24 (–27.87 to 5.64); 5.73 | –27.22 (–26.36 to –3.89); 13.15 |
| Victim injured | 191.73 (–104.99 to 723.32); 230.06 | 0.96 (–1.24 to 3.68); 1.24 | 0.16 (–0.66 to 1.25); 0.51 | 16.81 (–58.23 to 115.72); 45.02 | 217.61 (–13.46 to 739.37); 232.84 | 12.42 (0.30 to 26.43); 6.67 | 4.64 (–3.41 to 15.72); 4.98 | –19.82 (–36.98 to –5.74); 7.9 | –41.04 (–76.31 to 11.49); 16.5 |
| Respondent injured | 216.95 (–68.87 to 728.75); 219.60 | 1.24 (–1.16 to 3.92); 1.34 | 0.44 (–0.58 to 1.67); 0.58 | 2.64 (–58.69 to 93.26); 40.82 | 221.31 (–8.47 to 758.45); 230.41 | 19.95 (7.68 to 32.36); 6.64 | –0.31 (–5.55 to 5.48); 2.77 | –24.83 (–44.32 to –8.63); 9.33 | –3.49 (–62.20 to 63.78); 32.4 |
| Police involved in the incident | 288.02 (–54.47 to 876.67); 257.43 | 2.03 (–0.71 to 5.18); 1.49 | 0.12 (–0.84 to 1.40); 0.57 | 41.47 (–39.09 to 162.56); 50.85 | 242.23 (–10.79 to 761.04); 237.81 | 12.82 (–0.60 to 28.26); 7.25 | 1.50 (–4.41 to 7.76); 3.03 | –16.57 (–34.55 to –1.54); 8.56 | 4.42 (–55.66 to 94.21); 39.55 |
| Covariate | Observed (95% CI); SE (£) | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Total costs | GP non-psychiatric cost | GP psychiatric cost | Inpatient non-psychiatric cost | Inpatient psychiatric cost | Outpatient non-psychiatric cost | Outpatient psychiatric cost | Day care services cost | Community care cost | |
| Any violence | 86.12 (–41.83 to 247.93); 76.10 | 1.04 (–0.14 to 2.34); 0.64 | 0.75 (0.24 to 1.40); 0.29 | –0.68 (–58.78 to 72.47); 34.71 | 64.63 (–9.08 to 219.60); 69.87 | 10.78 (3.81 to 17.95); 3.65 | 3.44 (0.04 to 8.28); 2.08 | –10.76 (–20.81 to –2.81); 4.64 | 16.92 (–31.50, to 51.95) 16.46 |
| Violent while intoxicated | 163.69 (–56.96 to 520.92); 158.56 | 1.30 (–0.43 to 3.33); 0.97 | 1.35 (0.43 to 2.46); 0.52 | –16.64 (–82.75 to 57.86); 36.59 | 156.87 (–8.59 to 508.99); 163.57 | 8.36 (–1.50 to 19.75); 5.47 | 3.61 (0.06 to 8.03); 2.05 | –8.00 (–19.53 to 3.09); 5.64 | 16.85 (–12.51 to 59.37); 18.40 |
| Five or more violent incidents | 372.12 (–19.45 to 1033.20); 292.19 | 3.57 (0.37 to 7.11); 1.77 | 1.18 (–0.08 to 2.80); 0.72 | 10.38 (–59.89 to 96.06); 39.80 | 296.45 (–7.43 to 951.00); 298.01 | 19.18 (4.78 to 35.68); 7.89 | 1.94 (–1.36 to 6.40); 2.00 | –9.30 (–18.56 to –1.23); 4.26 | 48.73 (–14.45 to 163.79); 48.67 |
| Minor violence | –4.57 (–108.01 to 145.41); 65.88 | –0.31 (–1.74 to 0.99); 0.71 | 0.45 (–0.23 to 1.36); 0.40 | 37.77 (–60.60 to 182.08); 63.96 | –15.89 (–42.46 to –0.83); 12.30 | –4.20 (–11.67 to 3.00); 3.77 | –0.20 (–2.37 to 2.87); 1.34 | –10.20 (–16.58 to –4.53); 3.16 | –11.99 (–27.84 to 4.31); 8.16 |
| Victim injured | 219.59 (–46.40 to 674.43); 200.89 | 1.74 (–0.49 to 4.33); 1.21 | 0.99 (0.09 to 2.24); 0.55 | 5.76 (–73.58 to 100.60); 43.28 | 209.30 (–9.44 to 646.00); 196.99 | 17.27 (5.84 to 30.13); 6.44 | 8.21 (0.63 to 20.49); 5.25 | –9.20 (–19.32 to –0.43); 4.70 | –6.47 (–23.75 to 11.97); 8.93 |
| Respondent injured | 258.79 (–7.07 to 716.74); 201.71 | 2.13 (–0.14 to 4.63); 1.25 | 1.34 (0.31 to 2.64); 0.62 | –6.67 (–73.87 to 78.30); 38.60 | 205.44 (–4.02 to 661.61); 206.58 | 23.49 (12.42 to 38.97); 6.93 | 3.61 (–0.65 to 8.83); 2.51 | –11.15 (–19.66 to –3.93); 4.11 | 38.59 (–9.48 to 105.61); 29.16 |
| Police involved in the incident | 312.23 (–5.25 to 820.93); 226.66 | 2.74 (0.18 to 6.15); 1.47 | 0.92 (–0.09 to 2.26); 0.59 | 32.96 (–51.90 to 136.86); 49.97 | 229.18 (–6.26 to 744.82); 232.45 | 17.37 (4.06 to 31.08); 7.06 | 5.05 (–0.19 to 11.50); 3.04 | –6.80 (–17.56 to 5.22); 5.77 | 30.82 (–20.44 to 117.83); 37.88 |
Individuals who reported injuries to the victim or to themselves had outpatient non-psychiatric costs that were on average £12.42 and £19.95 higher, respectively, than for those with no such violence reported (see Table 87). Any violence was related to day care activity costs that were on average £21.88 lower than for those reporting no violence. Similar findings were observed for five or more violent incidents, the respondent being injured, the victim being injured or police involvement. Minor violence and the victim being injured were significantly associated with lower community service costs. No significant violence predictors were observed for total costs, GP psychiatric and non-psychiatric costs, inpatient non-psychiatric costs and outpatient psychiatric costs.
With psychiatric morbidity not controlled for, any violence was significantly associated with higher GP psychiatric and outpatient psychiatric and non-psychiatric costs and lower day care activity costs (see Table 88). Violence while intoxicated was associated with increased GP and outpatient psychiatric costs. Reporting more than five violent acts was significantly associated with higher GP and outpatient non-psychiatric costs. Minor violence was associated with lower psychiatric inpatient and day care costs. Violence in which the victim was injured was associated with increased GP psychiatric and outpatient psychiatric and non-psychiatric costs. Day care activity and community service costs were reduced for this group. If the respondent was injured then GP psychiatric and outpatient non-psychiatric costs were significantly increased whereas day care activity costs were reduced. Finally, if police involvement was reported then GP and outpatient non-psychiatric costs were increased.
Conclusions
The key strengths of these analyses were that service use and costs were derived from a nationally representative sample and disaggregated into subsets of costs (psychiatric and non-psychiatric) and the impact of violence variables on service costs were then examined in these subsets of costs. The analyses found that participants who reported violent behaviour used more GP and inpatient services for psychiatric reasons but had less contact with outpatient, day care and community care services.
From the multivariate analyses, no violence variable was found to be a statistically significant predictor of total costs despite it appearing to have a strong impact when univariate analyses were conducted. The likely explanation for this is that, at the aggregated level, the direct impact of violence cannot be distinguished from the impact of other demographic and clinical characteristics. Violence predicted lower costs for day care services. This is consistent with the result showing that individuals reporting violence were less likely to have day care contacts than those not reporting violence. Use of community services also appeared to be adversely affected by violence. This suggests that, although violence does not seem to affect access to health care, it does seem to reduce access to more ‘social’ services such as day care and community services. The health-care consequences of such reduced access should be studied.
The study found violence to be correlated with psychiatric comorbidity. In the first set of regression analyses, which controlled for psychiatric comorbidity (see Table 87), only a few violence variables were found to be statistically significant predictors of costs. However, in the second regression analysis in which psychiatric comorbidity was not controlled for (see Table 88), the number of violence variables that were statistically significant predictors of costs doubled. There is likely to be a complex relationship between violence and psychiatric morbidity and further work on the additional link with costs should also be a research priority.
There are a number of limitations of these analyses. First, the survey did not collect extensive information on GP contacts (focusing on the previous 2 weeks may be inadequate) and no information was available on the number of non-psychiatric outpatient visits. Extrapolating GP contacts to 3 months is likely to have resulted in an underestimate for some participants and an overestimate for others. For outpatient contacts, an external source was used as a proxy for this information and the same figure was applied across the whole sample. Second, although the sample was large the number of participants in the inpatient psychiatric group was relatively small and this may have restricted the analytical power of the analysis conducted. Third, the cost of medication was not considered in the analysis although individuals with a psychiatric condition are likely to be on some form of medication. If these costs were to be included the service costs would have been higher than the costs estimated, but it is unclear what any association with violence would be. Fourth, the analysis was based on self-report data of service use and this may have been subject to some inaccuracies. Likewise, self-reported violence may not always have been accurate.
In conclusion, this component of the study has shown that there is not a clear-cut impact of violence on health-care costs. Some service costs do seem to be increased, whereas access to other services seems to be reduced if violence occurs and this results in cost reductions.
Section B Severe mental illness and risk of violence
Chapter 12 Incidence cases of psychosis
Background
Prediction, prevention and punishment of violence frequently dominate public discussion and require significant public resources. Rates of violent crime rise and fall and are related to numerous factors. However, there remains a particular fear of violence perpetrated by those with mental illness, especially those diagnosed with schizophrenia, major depression or bipolar disorder. Studies show that up to 75% of the public believe that people with a mental illness are dangerous. 273,274 Media coverage of mental illness most often focuses on violence and crime,275–277 therefore encouraging fear of the mentally ill within our communities. 278,279 People with mental illness are among the most stigmatised groups in society280,281 and may internalise such stigma, resulting in reduced self-esteem and self-efficacy. 282 A perception that people with mental illness are inherently violent undoubtedly contributes to this well-documented stigma. 283
However, violence to others is a leading public health concern. To the extent that mental illness raises the risk for violence, those with mental illness in the community will be victimised. Often, when mentally ill individuals are violent, the victims of their violence are family members, who therefore may bear a disproportionate risk of victimisation and personal suffering. 284
It is widely expected by policy-makers and the public that assessment of violence risk in patients with mental illness should be a core skill and responsibility of mental health professionals. Violence risk assessment plays an important role in mental health law worldwide and ‘dangerousness to others’ is a key criterion for civil and forensic commitment in most jurisdictions. Imposition of tort liability on mental health professionals who negligently fail to predict, manage and prevent a patient’s violence towards others has become common.
The correct identification of those at risk for future violence is, therefore, of utmost importance to:
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protect the public and
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minimise additional stigmatisation among those with mental illness.
However, despite major developments and improvements in the assessment of risk for future violence, currently available risk assessment instruments still suffer from many shortcomings.
Accuracy of the prediction of future violence
State-of-the-art risk assessment instruments can be divided into two groups:
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actuarial instruments
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SPJ.
Actuarial instruments such as the Violence Risk Appraisal Guide (VRAG)285 or the Static-99286 have been developed on the basis of risk factors that are empirically related to violent behaviour. Risk factors included in these instruments are predominantly static but relatively simple to code. The codings of the items relating to risk factors are added up according to a fixed algorithm and conclusions with regard to the level of risk are based on the total score.
Structured professional judgement instruments such as the HCR-20287 are administered by experienced mental health professionals utilising a standardised checklist that contains empirically derived historical and dynamic risk factors for violence. The final risk judgement, however, is not based on a fixed algorithm but on expert decision-making. Risk factors are critically examined, combined and integrated to reach a conclusion. However, to validate these SPJ instruments, scores have to be assigned to allow a classification of level of risk that then can be tested using appropriate methods.
It is accepted that structured risk assessment instruments (ARA instruments and SPJ) outperform clinical judgement in the accuracy of the prediction of violent behaviour. 288,289 However, it has been previously demonstrated that most of these instruments achieve only a fair level of predictive accuracy (AUC values of 0.7) in comparison studies between more than one instrument. 8 Furthermore, most items in three instruments in one study [Psychopathy Checklist – Revised (PCL-R), VRAG, HCR-20] were not independently predictive of future violence and their predictive power was based on only a small number of their items. 23 Clinicians should be aware of these limitations and be critical when using either an actuarial or a SPJ instrument if the intention is to carry out a comprehensive assessment of risk on which to base subsequent risk management or treatment interventions. Moreover, the percentage correctly classified (PCC), which reflects the percentage of cases correctly classified (true positive and true negative) in the prediction of violence using either actuarial or SPJ instruments, is usually around 60%. 290 This implies that if a clinician relies on classification of risk based on these instruments, in approximately 40% of cases this classification will be wrong.
Causal compared with predictive models of risk for future violence
Most research carried out in the field of violence risk assessment utilises a predictive approach to either identify risk factors for violence or assess the predictive accuracy of instruments. Prediction requires temporal ordering of exposure (risk factors) and outcome (violence). Risk factors measured at some time point are investigated to see whether or not they are associated with violence occurring in a subsequent time window. This time window can cover a few weeks, months or several years. With regard to static, unchangeable risk factors, the choice of time frame following assessment should not matter. Static risk factors should equally predict violence occurring within the subsequent month and violence occurring within the subsequent year. However, when choosing the subsequent time frame for violent outcome after assessment of dynamic risk it is rarely taken into consideration that dynamic risk factors vary over time. Symptoms of mental illness are dynamic in nature and fluctuate. When investigating the association between mental illness and violent behaviour it is therefore essential to establish that a person was symptomatic when the violent incident occurred. Predictors derived from studies measuring symptoms or diagnoses at various points over the lifetime and comparing them with self-report or criminal records over extended periods cannot establish valid associations. Furthermore, because acute psychotic symptoms may present for relatively short periods, predictors that are identified over the lifespan may not be specific for psychosis and may apply equally to incidents of violence among the general (non-psychotic) population.
In the large National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), it was demonstrated that the incidence of violence was higher for people with severe mental illness, but only significantly for those with co-occurring substance abuse and/or dependence. 112 Multivariate analyses revealed that severe mental illness alone did not predict future violence; it was associated instead with historical, clinical, dispositional and contextual factors. However, most of these factors were more prevalent in people with severe mental illness.
However, reanalysis of NESARC data produced completely different results and revealed a positive (moderate) association between major mental illness and violence, emphasising that temporal closeness between dependent (violence) and independent (symptoms of mental illness) variables is key when investigating these relationships. 117
The MacArthur Violence Risk Assessment Study (MVRAS) has been one of the most influential studies on the association between mental illness and violence. A key finding of this study was that delusions do not predict violence among recently discharged psychiatric patients and this conclusion had a profound negative impact on research in this field. 20 Reanalysis confirmed that delusions (present in the past 10 weeks) are not predictors of subsequent violent behaviour (in the following 10 weeks). Redefinition of the time frame of occurrence considering temporal proximity, however, revealed strong associations between violence and delusional beliefs implying threat to the individual. 21 These results supported the findings of a previous study in which delusions of conspiracy and being spied on and persecutory delusions (implying threat to the symptomatic individual) were associated with serious violent behaviour in patients presenting with a first episode of psychotic illness. 291 However, in both studies the pathway from delusional beliefs towards violence was not direct; the key explanatory variable was anger as a result of delusions. These findings suggest that, when investigating the association between symptoms of mental illness and psychosis, different and complex pathways have to be taken into consideration.
Rationale for constructing a new instrument
Based on the shortcomings of currently available risk assessment instruments we considered it crucial to develop a new instrument addressing the problems and issues identified in recent research in the field of violence risk. Our main focus was identification of symptoms of mental illness that are causal, dynamic risk factors for violence in those with affective and non-affective psychotic illness and that are amenable to treatment in adult forensic and general psychiatric settings. We also aimed to create a static risk instrument for those with psychosis to inform clinicians about their patients’ propensity towards future violence. These static, historical factors may be unchangeable but can guide clinicians in their decision-making process.
Previous research has shown that risk factors differ substantially in men and women. It appears that clinical risk factors predict violence in women with sufficient accuracy, whereas criminogenic and criminal history variables are better at identifying men at risk for violence. 292 We therefore considered it essential to develop two modules to address sex differences in risk for violent behaviour.
Most importantly, we aimed not only to sensitise clinicians to symptom constellations in their patients that increase the risk of violence but also to advise action if a patient presents with these symptoms. Currently available ARA instruments classify patients only with regard to the level of risk for violence and SPJ instruments aim to help clinicians to understand the risk for violence in their patients. However, administration of risk assessment instruments (considering that these assessments are sometimes very time-consuming and require extensive training of those who administer them) should lead to appropriate management and, as a consequence, to a reduction in violent behaviour. The only study, however, that investigated whether or not administration of a SPJ instrument led to actual prevention of criminal and violent recidivism came to the conclusion ‘that the primary goal of preventing recidivism was not reached through risk assessment embedded in shared decision-making’ (p. 365). 18
Objectives
Our objectives were to:
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identify symptoms of mental illness that are causal risk factors in those patients with psychosis and amenable to treatment in forensic and general psychiatric settings
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develop a static risk instrument for future violence to inform about patients’ propensities for future violence.
Method
Study design and sample
Baseline study
The East London First Episode Psychosis Study (ELFEPS)291 was carried out between December 1996 and December 2000 in the London boroughs of City and Hackney, Tower Hamlets and Newham. All those aged 16–84 years living in the study area who made contact with mental health services (including adult community health teams, inpatient units, forensic services, learning disability services, adolescent mental health services and drug and alcohol units) because of a first episode of any probable psychotic disorder were identified and screened. Initial inclusion criteria were based on a World Health Organization (WHO) study293 and the Ætiology and Ethnicity in Schizophrenia and Other Psychoses (AESOP) study. 294 Methods used by Cooper et al. 295 were used to minimise leakage and identify patients missed by screening. Patients who passed the screen underwent a battery of assessments. The Schedules for Clinical Assessment in Neuropsychiatry (SCAN)296 make up a set of instruments used to assess adult major psychiatric disorders. Three clinical research fellows carried out the data collection and were trained in the SCAN interview by taking a course approved by the WHO. Prestudy reliability was established using independent ratings of videotaped interviews. ICD-10 and DSM-IV diagnoses were allocated by consensus agreement between the principal investigator (JWC) and the research team.
Overall, 490 individuals were recruited [City and Hackney, n = 167 (34.1%); Tower Hamlets, n = 166 (33.9%); Newham, n = 157 (32.0%)]. More than half of those recruited were men [n = 302 (61.6%)] and the sample was ethnically diverse [white, n = 179 (36.5%); black, n = 165 (33.7%); Asian, n = 117 (23.9%); other, n = 29 (5.9%)]. Approximately half of the study participants were not born in the UK [n = 243 (49.6%)]. The mean age of the sample at baseline was 30.5 years (SD 10.1 years).
The most prevalent consensus diagnosis at baseline was schizophrenia (34.3%). Schizotypal personality disorder was diagnosed in 0.4% of the sample, delusional disorder in 5.9%, acute/transient psychosis in 9.6%, schizoaffective disorder in 18.6% and other non-affective psychosis in 6%. Approximately 24% of the study participants presented with affective psychosis including unipolar (14%), bipolar (10.4%) and other (0.8%) affective psychoses.
Follow-up study
Data collection commenced in January 2010 and was finished on 30 June 2013. The study was granted Section 251 (NHS Act 2006297) approval from the National Information Governance Board (NIGB) to gather data without the consent of the baseline study participants. The design was a retrospective case note study that aimed to cover 10 years after the initial assessment at baseline.
We utilised a multitude of resources including medical records in 31 primary and 20 secondary care trusts across England, the NHS databases SPINE and RIO to identify if and when participants exited the NHS and the death register to identify the proportion of participants who died during follow-up and their cause of death. The Police National Computer (PNC), an operational police database containing criminal histories of all offenders in England, Wales and Scotland, was searched in January 2012 by the Ministry of Justice to gather information on criminal convictions and cautions of the sample.
Data were collected by research assistants and clinical studies officers from the Primary Care Research Network and the Mental Health Research Network.
We obtained complete 10-year follow-up data for 74% of the sample and complete 5-year follow-up data for 81% of the sample. At least 1 year of follow-up data were available for 95.1% of the sample. Thirty-four participants (6.9%) died at some point during the follow-up period. Causes of death included eight suicides and in three cases the cause of death was unascertainable. The majority of the deceased died of natural causes. Loss to follow-up mostly occurred because baseline participants had exited the NHS (mostly to return to their country of origin). In a few cases the GP surgery where a patient was registered refused access to his or her medical records.
Measures
Static risk factors
Static risk factors were assessed at baseline using a standardised interview schedule with operational definitions for all ratings.
Symptoms of mental illness: dynamic risk factors
The OPerational CRITeria checklist (for psychotic and affective symptoms) (OPCRIT) system298 was administered to collect information on a broad range of symptoms of mental illness. The checklist was specifically designed for the needs of empirical research and demonstrated good reliability and validity. 299 Episodes were dated and differentiated by a 2-month symptom-free interval for affective symptoms and a 6-month symptom-free interval for psychotic symptoms. Symptom domains relevant for the study and coded were appearance and behaviour (e.g. bizarre behaviour, catatonia), speech and form of thought (formal thought disorder), affect and associated features, abnormal beliefs and ideas (delusions) and abnormal perceptions (hallucinations).
Outcome
The MacArthur Community Violence Instrument (MCVI)300 was rated based on case notes in primary and secondary care. Actions were considered to constitute serious violence if they were:
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batteries that resulted in physical injury or involved the use of a weapon
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sexual assaults
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threats made with a weapon in hand.
Batteries not resulting in injury of the victim were considered as minor violence.
Further outcome data were derived from convictions and cautions recorded in the PNC, an operational police database containing criminal histories of all offenders in England, Wales and Scotland. For categorisation of violent offences, we used offences in the Home Office’s Standard List301 for definition of violence (committed) plus threats to commit such an offence for England, Wales and Scotland.
To ensure sufficient statistical power, violent incidents derived from medical records and PNC data were combined into one outcome measure: ‘violent behaviour’. Because of small numbers, it was not possible to differentiate minor and serious violence.
Statistical analyses
For descriptive purposes, absolute (n) and relative (%) frequencies were reported for dichotomous/polytomous categorical variables and means and SDs were reported for variables at the interval/ratio level.
To ensure sufficient statistical power it was decided to divide the 10-year follow-up into 6-month windows. This resulted in up to 20 repeated measurements per study participant. All analyses were stratified to account for sex differences.
Static risk instrument
All static risk factors were binary and their predictive accuracy was assessed using the ROC using the ‘somersd’ command and ‘lincom’ for statistical significance in Stata. ROC plots display the areas of pairs of ‘sensitivity’ and ‘1 – specificity’ for each score. Predictive accuracy is quantified in a value known as the AUC. The AUC is equal to the probability that a randomly chosen violent person will score higher on the measure in question than a randomly chosen non-violent person. AUC values can range from 0.50 (no discrimination) to 1.00 (perfect discrimination); values exceeding 0.70 are considered large, with acceptable AUCs indicating greater predictive accuracy.
In a first step AUC values were computed for each of the 20 time windows to investigate their stability over time. The static risk model was then developed utilising the first 6-month time window after the baseline assessment. To avoid shrinkage when applying the instrument to a different, external sample we decided to keep the model as simple as possible. Based on a forward selection process the highest AUC values were identified and subsequently added up. The magnitude of the AUC value, sensitivity, specificity and PCC were utilised to identify the optimal cut-off point. The stability of the predictive accuracy over time was then tested by adding 6-month time windows cumulatively.
Dynamic risk factors
To take advantage of the longitudinal study design, multilevel modelling was applied. These models account for dependence of data collected longitudinally by modelling the relatedness of repeated measurements within the same individual as random effects. Unlike other approaches, mixed-effect (multilevel) models do not require that data are complete for individuals at each time point or require imputation of data, which may result in bias. 302 By making use of all available data, multilevel models are therefore particularly powerful in longitudinal studies in which the data are often incomplete.
Logistic mixed-effect models (‘melogit’ in Stata) were applied to investigate associations between symptoms of mental illness and violent outcome. Data from all 20 study periods were included in the analyses. To estimate the effects of exposure on outcome over the entire study period regardless of time point, we included time as a covariate. The logistic mixed-effect models therefore provided a single estimate (OR), 95% CIs and a significance value of the relationship between symptoms and violence over the entire course of the study.
To ensure temporal proximity between dependent and independent variables, we investigated the associations between exposure and outcome occurring in the same time period.
As the mixed-model approach resulted in a substantial increase in statistical power we decided to adopt a conservative approach in the selection of confounding variables. All static risk factors were entered separately and tested in the total, male and female samples (Table 89). As subsequent moderation analyses required the inclusion of the total sample, variables significantly associated with violence in the total sample were adjusted for throughout, including age < 35 years, black ethnic origin, no educational qualifications, poverty and parental discord before the age of 15 years, history of violent behaviour leading to contact with services, threatening or annoying behaviour leading to contact with services, violent and non-violent offending, family history of criminal behaviour, a diagnosis of schizophrenia at first presentation to services, high level of trait anger, long duration of untreated psychosis, conduct disorder before the age of 15 years and drug use in the past year.
| Static risk factors | Total | Men | Women | ||||||
|---|---|---|---|---|---|---|---|---|---|
| AORa | 95% CI | p-value | AORa | 95% CI | p-value | AORa | 95% CI | p-value | |
| Demography | |||||||||
| Male sex | 2.56 | 1.61 to 4.07 | < 0.001 | ||||||
| Age < 35 years | 2.58 | 1.53 to 4.35 | < 0.001 | 2.74 | 1.47 to 5.12 | 0.002 | 1.86 | 0.76 to 4.57 | 0.177 |
| Black ethnicity | 1.33 | 0.86 to 2.06 | 0.206 | 1.48 | 0.90 to 2.44 | 0.126 | 0.73 | 0.31 to 1.75 | 0.480 |
| Single marital status | 1.54 | 1.00 to 2.37 | 0.050 | 1.27 | 0.75 to 2.15 | 0.384 | 1.24 | 0.57 to 2.69 | 0.586 |
| UK born | 1.45 | 0.95 to 2.22 | 0.083 | 1.24 | 0.76 to 2.03 | 0.388 | 1.65 | 0.77 to 3.57 | 0.200 |
| Asylum seeker/refugee | 0.91 | 0.48 to 1.72 | 0.772 | 0.81 | 0.41 to 1.61 | 0.546 | 0.75 | 0.17 to 3.37 | 0.705 |
| No educational qualifications | 1.77 | 1.15 to 2.72 | 0.010 | 1.66 | 1.01 to 2.73 | 0.045 | 1.73 | 0.77 to 3.84 | 0.182 |
| Three or more moves of residence | 1.33 | 0.85 to 2.08 | 0.207 | 1.52 | 0.92 to 2.51 | 0.104 | 0.79 | 0.33 to 1.89 | 0.591 |
| Not worked in the past year | 1.01 | 0.65 to 1.55 | 0.976 | 1.20 | 0.73 to 1.97 | 0.463 | 0.77 | 0.34 to 1.74 | 0.526 |
| Childhood adversities before 15 years of age | |||||||||
| In care/adopted/fostered | 0.45 | 0.19 to 1.05 | 0.065 | 0.63 | 0.24 to 1.68 | 0.360 | 0.25 | 0.04 to 1.45 | 0.123 |
| Poverty | 1.74 | 1.06 to 2.87 | 0.030 | 1.69 | 0.96 to 2.96 | 0.068 | 1.56 | 0.58 to 4.18 | 0.373 |
| Parental discord | 2.40 | 1.51 to 3.80 | < 0.001 | 2.58 | 1.53 to 4.35 | < 0.001 | 1.65 | 0.67 to 4.08 | 0.278 |
| Cruelty/physical abuse | 1.38 | 0.69 to 2.72 | 0.361 | 1.84 | 0.86 to 3.94 | 0.119 | 0.51 | 0.10 to 2.54 | 0.415 |
| Sexual abuse | 0.58 | 0.21 to 1.62 | 0.298 | 1.67 | 0.52 to 5.43 | 0.392 | – | ||
| Criminogenic factors | |||||||||
| History of violent behaviour leading to contact with services | 2.48 | 1.62 to 3.79 | < 0.001 | 2.22 | 1.37 to 3.60 | 0.001 | 2.02 | 0.87 to 4.72 | 0.101 |
| History of threatening/annoying behaviour leading to contact with services | 2.70 | 1.70 to 4.28 | < 0.001 | 2.49 | 1.43 to 4.34 | 0.001 | 2.16 | 0.98 to 4.77 | 0.057 |
| History of violent offending | 3.74 | 2.14 to 6.53 | < 0.001 | 2.65 | 1.46 to 4.81 | 0.001 | 8.18 | 1.98 to 33.82 | 0.004 |
| History of non-violent offending | 2.73 | 1.76 to 4.24 | < 0.001 | 2.23 | 1.38 to 3.59 | 0.001 | 1.80 | 0.44 to 7.41 | 0.417 |
| Family history of criminal behaviour | 1.96 | 1.01 to 3.81 | 0.048 | 2.17 | 1.04 to 4.54 | 0.040 | 1.15 | 0.28 to 4.70 | 0.845 |
| Clinical factors | |||||||||
| Schizophrenia at first presentation | 1.69 | 1.10 to 2.60 | 0.016 | 1.31 | 0.80 to 2.16 | 0.283 | 2.63 | 1.23 to 5.64 | 0.013 |
| High trait impulsiveness | 1.76 | 0.89 to 3.48 | 0.104 | 1.89 | 0.93 to 3.83 | 0.077 | – | – | – |
| High trait anger | 2.81 | 1.51 to 5.21 | 0.001 | 3.09 | 1.62 to 5.91 | 0.001 | 0.35 | 0.03 to 3.60 | 0.373 |
| Long duration of untreated psychosis | 0.21 | 0.05 to 0.86 | 0.030 | 0.27 | 0.06 to 1.14 | 0.075 | – | – | – |
| Conduct disorder | 1.98 | 1.18 to 3.33 | 0.010 | 1.78 | 1.03 to 3.07 | 0.038 | 0.53 | 0.08 to 3.45 | 0.509 |
| Alcohol abuse past year | 0.97 | 0.48 to 1.92 | 0.921 | 0.60 | 0.29 to 1.26 | 0.181 | 4.05 | 0.74 to 22.10 | 0.106 |
| Drug use past year | 1.79 | 1.18 to 2.70 | 0.006 | 1.40 | 0.86 to 2.28 | 0.175 | 1.69 | 0.75 to 3.76 | 0.203 |
| Family history of severe mental illness | 1.43 | 0.91 to 2.24 | 0.123 | 1.25 | 0.75 to 2.09 | 0.393 | 1.52 | 0.65 to 3.57 | 0.333 |
| Family history of substance abuse | 1.26 | 0.63 to 2.48 | 0.514 | 1.24 | 0.61 to 2.54 | 0.556 | 0.32 | 0.03 to 3.27 | 0.337 |
To account for co-occurrence, dynamic risk factors from the same symptom domain were adjusted for each other.
Mediation analyses to identify indirect pathways were carried out by testing the required triangle associations: statistically significant relationship between (1) independent (dynamic risk factor: symptom of mental illness) and dependent (violence) variables; (2) independent and hypothesised mediator (affect) variables; and (3) mediator and dependent variables. By comparing standardised regression coefficients from models with and without mediator as a covariate,303 we estimated the proportion of direct effects that were mediated and tested their significance using bootstrapped SEs and CIs (using 1000 repetitions). This method is preferred over other tests for significant indirect effects, such as the Sobel test, because it is less conservative and does not require normality assumptions to be met. 304
Moderation analyses were performed to investigate effect modification. This was applied when testing sex differences in the association between dynamic risk factors and violence and the effects of static risk level. A multiplicative term was included in the statistical models.
All statistical analyses were conducted in Stata SE.
An alpha level of p < 0.05 was adopted throughout.
Results
Violent outcome
Violent behaviour demonstrated great fluctuation over the 10-year follow-up (Figure 5). Unsurprisingly, the prevalence of violent behaviour was consistently higher among men. For both sexes prevalence was highest in the first 6 months following inclusion in the study.
FIGURE 5.
Violent behaviour across 10 years of follow-up.
Static risk instrument
Overall, 27 static factors from four domains were included. The demography domain covered age < 35 years, black ethnicity, single marital status, UK born, asylum seeker, no educational qualifications, three or more moves of residence in the past year and not worked in past year. Childhood adversities covered care/adopted/fostered, poverty, parental discord, cruelty/physical abuse and sexual abuse. Criminogenic variables were a history of violent offending leading to contact, a history of threatening/annoying behaviour leading to contact, a history of violent offending, a history of non-violent offending and a history of family criminal behaviour. In the clinical domain, the variables covered were schizophrenia at first presentation, high trait impulsiveness, high trait anger, long duration of untreated psychosis, conduct disorder, alcohol abuse in the past year, drug use in the past year, a family history of mental illness and a family history of substance abuse.
In a first step we calculated AUC values for each 6-month window for each of the static factors. As can be seen in Tables 90 and 91, there was great variation across time and between sexes.
| Static risk factors | Months | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | 12 | 18 | 24 | 30 | 36 | 42 | 48 | 54 | 60 | 66 | 72 | 78 | 84 | 90 | 96 | 102 | 108 | 114 | 120 | |
| Demography | ||||||||||||||||||||
| Age < 35 years | 0.48 | 0.58 | 0.63 | 0.47 | 0.63 | 0.63 | 0.58 | 0.62 | 0.63 | 0.63 | 0.63 | 0.48 | 0.63 | 0.63 | 0.62 | 0.57 | 0.56 | 0.58 | 0.57 | 0.62 |
| Black ethnicity | 0.55 | 0.49 | 0.58 | 0.52 | 0.54 | 0.61 | 0.52 | 0.49 | 0.47 | 0.61 | 0.66 | 0.54 | 0.54 | 0.51 | 0.42 | 0.66 | 0.38 | 0.65 | 0.47 | 0.32 |
| Single marital status | 0.48 | 0.57 | 0.47 | 0.56 | 0.51 | 0.47 | 0.50 | 0.49 | 0.51 | 0.58 | 0.57 | 0.44 | 0.59 | 0.47 | 0.56 | 0.43 | 0.52 | 0.47 | 0.55 | 0.14 |
| UK born | 0.50 | 0.55 | 0.41 | 0.47 | 0.65 | 0.54 | 0.57 | 0.38 | 0.62 | 0.72 | 0.55 | 0.35 | 0.57 | 0.46 | 0.61 | 0.54 | 0.47 | 0.63 | 0.46 | 0.21 |
| Asylum seeker | 0.53 | 0.50 | 0.61 | 0.41 | 0.49 | 0.47 | 0.48 | 0.60 | 0.48 | 0.43 | 0.51 | 0.50 | 0.43 | 0.56 | 0.53 | 0.54 | 0.49 | 0.47 | 0.48 | 0.43 |
| No educational qualifications | 0.42 | 0.59 | 0.59 | 0.51 | 0.64 | 0.54 | 0.56 | 0.38 | 0.62 | 0.42 | 0.63 | 0.42 | 0.64 | 0.59 | 0.61 | 0.49 | 0.46 | 0.58 | 0.56 | 0.71 |
| Three or more moves of residence in the past year | 0.58 | 0.58 | 0.58 | 0.42 | 0.55 | 0.62 | 0.49 | 0.67 | 0.64 | 0.77 | 0.33 | 0.40 | 0.55 | 0.58 | 0.52 | 0.49 | 0.45 | 0.55 | 0.53 | 0.32 |
| Not worked in the past year | 0.54 | 0.51 | 0.39 | 0.67 | 0.50 | 0.40 | 0.57 | 0.55 | 0.47 | 0.58 | 0.39 | 0.51 | 0.37 | 0.60 | 0.52 | 0.50 | 0.48 | 0.40 | 0.64 | 0.73 |
| Childhood adversities before 15 years of age | ||||||||||||||||||||
| In care/adopted/fostered | 0.45 | 0.54 | 0.45 | 0.50 | 0.53 | 0.60 | 0.46 | 0.46 | 0.46 | 0.53 | 0.45 | 0.45 | 0.46 | 0.52 | 0.45 | 0.45 | 0.52 | 0.50 | 0.45 | 0.45 |
| Poverty | 0.54 | 0.57 | 0.46 | 0.65 | 0.54 | 0.53 | 0.44 | 0.56 | 0.54 | 0.61 | 0.56 | 0.54 | 0.54 | 0.65 | 0.59 | 0.56 | 0.51 | 0.42 | 0.43 | 0.38 |
| Parental discord | 0.61 | 0.60 | 0.46 | 0.70 | 0.61 | 0.68 | 0.49 | 0.89 | 0.64 | 0.75 | 0.55 | 0.38 | 0.53 | 0.64 | 0.48 | 0.55 | 0.57 | 0.56 | 0.48 | 0.38 |
| Cruelty/physical abuse | 0.49 | 0.50 | 0.45 | 0.50 | 0.45 | 0.45 | 0.45 | 0.45 | 0.50 | 0.45 | 0.45 | 0.53 | 0.53 | 0.65 | 0.66 | 0.51 | 0.59 | 0.60 | 0.56 | 0.46 |
| Sexual abuse | 0.48 | 0.52 | 0.48 | 0.48 | 0.48 | 0.48 | 0.48 | 0.48 | 0.53 | 0.48 | 0.48 | 0.56 | 0.63 | 0.48 | 0.59 | 0.48 | 0.49 | 0.53 | 0.54 | 0.49 |
| Criminogenic factors | ||||||||||||||||||||
| History of violence leading to contact | 0.61 | 0.48 | 0.63 | 0.57 | 0.53 | 0.69 | 0.57 | 0.64 | 0.62 | 0.45 | 0.74 | 0.52 | 0.60 | 0.76 | 0.41 | 0.59 | 0.50 | 0.63 | 0.52 | 0.31 |
| History of threatening/annoying behaviour leading to contact | 0.61 | 0.55 | 0.54 | 0.52 | 0.60 | 0.63 | 0.57 | 0.49 | 0.62 | 0.45 | 0.59 | 0.68 | 0.67 | 0.60 | 0.57 | 0.61 | 0.41 | 0.62 | 0.51 | 0.66 |
| History of violent offending | 0.55 | 0.48 | 0.63 | 0.49 | 0.58 | 0.58 | 0.64 | 0.60 | 0.58 | 0.58 | 0.61 | 0.50 | 0.51 | 0.49 | 0.43 | 0.43 | 0.43 | 0.57 | 0.64 | 0.43 |
| History of non-violent offending | 0.49 | 0.51 | 0.53 | 0.49 | 0.70 | 0.62 | 0.59 | 0.67 | 0.74 | 0.55 | 0.68 | 0.55 | 0.62 | 0.58 | 0.74 | 0.55 | 0.59 | 0.61 | 0.47 | 0.32 |
| Family history of criminal behaviour | 0.53 | 0.54 | 0.46 | 0.61 | 0.60 | 0.51 | 0.51 | 0.46 | 0.51 | 0.60 | 0.46 | 0.60 | 0.53 | 0.52 | 0.46 | 0.45 | 0.58 | 0.55 | 0.56 | 0.45 |
| Clinical factors | ||||||||||||||||||||
| Schizophrenia at first presentation | 0.44 | 0.47 | 0.61 | 0.55 | 0.58 | 0.53 | 0.66 | 0.80 | 0.50 | 0.52 | 0.64 | 0.29 | 0.60 | 0.76 | 0.40 | 0.41 | 0.43 | 0.53 | 0.45 | 0.80 |
| High trait impulsiveness | 0.52 | 0.53 | 0.51 | 0.55 | 0.52 | 0.54 | 0.54 | 0.44 | 0.49 | 0.44 | 0.61 | 0.66 | 0.59 | 0.57 | 0.44 | 0.44 | 0.70 | 0.49 | 0.44 | 0.44 |
| High trait anger | 0.62 | 0.48 | 0.44 | 0.54 | 0.51 | 0.54 | 0.60 | 0.61 | 0.59 | 0.58 | 0.53 | 0.44 | 0.66 | 0.70 | 0.64 | 0.61 | 0.57 | 0.58 | 0.54 | 0.44 |
| Long duration of untreated psychosis | 0.47 | 0.47 | 0.47 | 0.47 | 0.47 | 0.47 | 0.52 | 0.47 | 0.47 | 0.54 | 0.47 | 0.47 | 0.54 | 0.47 | 0.47 | 0.47 | 0.47 | 0.47 | 0.47 | 0.47 |
| Conduct disorder | 0.53 | 0.56 | 0.39 | 0.44 | 0.54 | 0.58 | 0.54 | 0.39 | 0.65 | 0.38 | 0.48 | 0.46 | 0.75 | 0.64 | 0.69 | 0.56 | 0.58 | 0.62 | 0.59 | 0.39 |
| Alcohol abuse in the past year | 0.42 | 0.47 | 0.43 | 0.48 | 0.43 | 0.48 | 0.53 | 0.43 | 0.53 | 0.43 | 0.43 | 0.58 | 0.58 | 0.43 | 0.63 | 0.48 | 0.56 | 0.47 | 0.48 | 0.43 |
| Drug use in the past year | 0.47 | 0.47 | 0.54 | 0.48 | 0.59 | 0.70 | 0.52 | 0.56 | 0.63 | 0.58 | 0.56 | 0.29 | 0.65 | 0.60 | 0.52 | 0.44 | 0.47 | 0.69 | 0.57 | 0.72 |
| Family history of mental illness | 0.44 | 0.55 | 0.40 | 0.54 | 0.69 | 0.66 | 0.58 | 0.83 | 0.63 | 0.61 | 0.64 | 0.46 | 0.61 | 0.51 | 0.41 | 0.36 | 0.57 | 0.40 | 0.56 | 0.31 |
| Family history of substance abuse | 0.44 | 0.66 | 0.44 | 0.49 | 0.65 | 0.57 | 0.59 | 0.43 | 0.48 | 0.43 | 0.43 | 0.50 | 0.43 | 0.56 | 0.53 | 0.43 | 0.56 | 0.52 | 0.53 | 0.42 |
| Static risk factors | Months | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | 12 | 18 | 24 | 30 | 36 | 42 | 48 | 54 | 60 | 66 | 72 | 78 | 84 | 90 | 96 | 102 | 108 | 114 | 120 | |
| Demography | ||||||||||||||||||||
| Age < 35 years | 0.55 | 0.49 | 0.49 | 0.66 | 0.66 | 0.41 | 0.16 | – | 0.66 | 0.16 | 0.66 | 0.66 | 0.56 | 0.66 | 0.66 | 0.49 | 0.49 | 0.66 | 0.15 | 0.65 |
| Black ethnicity | 0.65 | 0.34 | 0.34 | 0.42 | 0.34 | 0.35 | 0.85 | – | 0.35 | 0.85 | 0.51 | 0.34 | 0.45 | 0.35 | 0.35 | 0.52 | 0.35 | 0.52 | 0.86 | 0.35 |
| Single marital status | 0.58 | 0.44 | 0.63 | 0.62 | 0.80 | 0.29 | 0.28 | – | 0.54 | 0.29 | 0.46 | 0.63 | 0.48 | 0.28 | 0.28 | 0.62 | 0.62 | 0.62 | 0.28 | 0.29 |
| UK born | 0.66 | 0.42 | 0.42 | 0.33 | 0.59 | 0.50 | 0.25 | – | 0.50 | 0.75 | 0.75 | 0.75 | 0.44 | 0.24 | 0.75 | 0.58 | 0.58 | 0.58 | 0.75 | 0.75 |
| Asylum seeker | 0.56 | 0.46 | 0.63 | 0.46 | 0.46 | 0.46 | 0.47 | – | 0.47 | 0.47 | 0.46 | 0.46 | 0.46 | 0.46 | 0.46 | 0.63 | 0.46 | 0.46 | 0.47 | 0.46 |
| No educational qualifications | 0.75 | 0.58 | 0.41 | 0.32 | 0.40 | 0.49 | 0.73 | – | 0.48 | 0.73 | 0.57 | 0.74 | 0.54 | 0.74 | 0.74 | 0.57 | 0.40 | 0.57 | 0.75 | 0.74 |
| Three or more moves of residence in the past year | 0.44 | 0.51 | 0.70 | 0.43 | 0.53 | 0.36 | 0.86 | – | 0.36 | 0.36 | 0.53 | 0.53 | 0.46 | 0.36 | 0.36 | 0.36 | 0.53 | 0.53 | 0.36 | 0.36 |
| Not worked in the past year | 0.68 | 0.51 | 0.33 | 0.41 | 0.15 | 0.66 | 0.15 | – | 0.40 | 0.65 | 0.49 | 0.49 | 0.35 | 0.65 | 0.65 | 0.31 | 0.31 | 0.48 | 0.65 | 0.65 |
| Childhood adversities before age 15 years | ||||||||||||||||||||
| In care/adopted/fostered | 0.43 | 0.43 | 0.44 | 0.43 | 0.43 | 0.43 | 0.43 | – | 0.43 | 0.43 | 0.60 | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 | 0.93 |
| Poverty | 0.62 | 0.42 | 0.59 | 0.42 | 0.42 | 0.42 | 0.92 | – | 0.42 | 0.42 | 0.76 | 0.58 | 0.41 | 0.92 | 0.42 | 0.58 | 0.42 | 0.58 | 0.42 | 0.92 |
| Parental discord | 0.60 | 0.40 | 0.57 | 0.57 | 0.91 | 0.40 | 0.40 | – | 0.65 | 0.40 | 0.57 | 0.40 | 0.60 | 0.40 | 0.40 | 0.56 | 0.56 | 0.57 | 0.40 | 0.90 |
| Cruelty/physical abuse | 0.45 | 0.45 | 0.63 | 0.54 | 0.63 | 0.45 | 0.45 | – | 0.45 | 0.45 | 0.45 | 0.45 | 0.45 | 0.45 | 0.45 | 0.45 | 0.62 | 0.45 | 0.45 | 0.45 |
| Sexual abuse | 0.45 | 0.45 | 0.45 | 0.45 | 0.45 | 0.45 | 0.45 | – | 0.45 | 0.45 | 0.45 | 0.45 | 0.45 | 0.45 | 0.45 | 0.45 | 0.45 | 0.45 | 0.45 | 0.45 |
| Criminogenic factors | ||||||||||||||||||||
| History of violence leading to contact | 0.58 | 0.38 | 0.71 | 0.63 | 0.54 | 0.88 | 0.38 | – | 0.88 | 0.88 | 0.38 | 0.38 | 0.48 | 0.88 | 0.38 | 0.54 | 0.37 | 0.54 | 0.88 | 0.38 |
| History of threatening/annoying behaviour leading to contact | 0.55 | 0.24 | 0.75 | 0.67 | 0.75 | 0.75 | 0.75 | – | 0.75 | 0.75 | 0.24 | 0.41 | 0.65 | 0.75 | 0.24 | 0.75 | 0.58 | 0.41 | 0.75 | 0.25 |
| History of violent offending | 0.49 | 0.49 | 0.66 | 0.58 | 0.83 | 0.49 | 0.49 | – | 0.49 | 0.49 | 0.49 | 0.49 | 0.59 | 0.49 | 0.49 | 0.66 | 0.66 | 0.49 | 0.49 | 0.49 |
| History of non-violent offending | 0.47 | 0.47 | 0.47 | 0.47 | 0.64 | 1.47 | 0.47 | – | 0.47 | 0.47 | 0.64 | 0.46 | 0.57 | 0.46 | 0.46 | 0.63 | 0.46 | 0.46 | 0.47 | 0.46 |
| Family history of criminal behaviour | ||||||||||||||||||||