Paranoia is receiving increasing attention in its own right, since it is a central experience of psychotic disorders and a marker of the health of a society. Paranoia is associated with use of the most commonly taken illicit drug, cannabis. The objective was to determine whether the principal psychoactive ingredient of cannabis—∆9-tetrahydrocannabinol (THC)—causes paranoia and to use the drug as a probe to identify key cognitive mechanisms underlying paranoia. A randomized, placebo-controlled, between-groups test of the effects of intravenous THC was conducted. A total of 121 individuals with paranoid ideation were randomized to receive placebo, THC, or THC preceded by a cognitive awareness condition. Paranoia was assessed extensively via a real social situation, an immersive virtual reality experiment, and standard self-report and interviewer measures. Putative causal factors were assessed. Principal components analysis was used to create a composite paranoia score and composite causal variables to be tested in a mediation analysis. THC significantly increased paranoia, negative affect (anxiety, worry, depression, negative thoughts about the self), and a range of anomalous experiences, and reduced working memory capacity. The increase in negative affect and in anomalous experiences fully accounted for the increase in paranoia. Working memory changes did not lead to paranoia. Making participants aware of the effects of THC had little impact. In this largest study of intravenous THC, it was definitively demonstrated that the drug triggers paranoid thoughts in vulnerable individuals. The most likely mechanism of action causing paranoia was the generation of negative affect and anomalous experiences.

Background and AimsViolent behaviour by forensic psychiatric inpatients is common. We aimed to systematically review the performance of structured risk assessment tools for violence in these settings.MethodsThe nine most commonly used violence risk assessment instruments used in psychiatric hospitals were examined. A systematic search of five databases (CINAHL, Embase, Global Health, PsycINFO and PubMed) was conducted to identify studies examining the predictive accuracy of these tools in forensic psychiatric inpatient settings. Risk assessment instruments were separated into those designed for imminent (within 24 hours) violence prediction and those designed for longer-term prediction. A range of accuracy measures and descriptive variables were extracted. A quality assessment was performed for each eligible study using the QUADAS-2. Summary performance measures (sensitivity, specificity, positive and negative predictive values, diagnostic odds ratio, and area under the curve value) and HSROC curves were produced. In addition, meta-regression analyses investigated study and sample effects on tool performance.ResultsFifty-two eligible publications were identified, of which 43 provided information on tool accuracy in the form of AUC statistics. These provided data on 78 individual samples, with information on 6,840 patients. Of these, 35 samples (3,306 patients from 19 publications) provided data on all performance measures. The median AUC value for the wider group of 78 samples was higher for imminent tools (AUC 0.83; IQR: 0.71–0.85) compared with longer-term tools (AUC 0.68; IQR: 0.62-0.75). Other performance measures indicated variable accuracy for imminent and longer-term tools. Meta-regression indicated that no study or sample-related characteristics were associated with between-study differences in AUCs.InterpretationThe performance of current tools in predicting risk of violence beyond the first few days is variable, and the selection of which tool to use in clinical practice should consider accuracy estimates. For more imminent violence, however, there is evidence in support of brief scalable assessment tools.

BackgroundBorderline personality disorder (BPD) is characterised by difficulties with impulse control and affective dysregulation. It is unclear whether BPD contributes to the perpetration of violence or whether this is explained by comorbidity. We explored independent associations between categorical and dimensional representations of BPD and violence in the general population, and differential associations from individual BPD criteria.MethodsWe used a representative combined sample of 14,753 men and women from two British national surveys of adults (≥16 years). BPD was assessed using the Structured Clinical Interview II- Questionnaire. We measured self-reported violent behaviour in the past 5 years, including severity, victims and locations of incidents. Associations for binary, dimensional and trait-level exposures were performed using weighted logistic regression, adjusted for demography and comorbid psychopathology.ResultsCategorical diagnosis of BPD was associated only with intimate partner violence (IPV). Associations with serious violence leading to injuries and repetitive violence were better explained by comorbid substance misuse, anxiety and antisocial personality disorder (ASPD). However, anger and impulsivity BPD items were independently associated with most violent outcomes including severity, repetition and injury; suicidal behaviours and affective instability were not associated with violence. Both trait-level and severity-dimensional analyses showed that BPD symptoms might impact males and females differently in terms of violence.ConclusionsFor individuals diagnosed BPD, violence is better explained by comorbidity. However, BPD individual traits show different pathways to violence at the population level. Gender differences in BPD traits and their severity indicate distinct, underlying mechanisms towards violence. BPD and traits should be evaluated in perpetrators of IPV.

BackgroundIdentification of biomarkers predicting therapeutic outcome of antidepressant treatment is one of the most important tasks in current research because it may transform the lengthy process of finding the right treatment for a given individual with depression. In the current study, we explored the potential of pretreatment pregenual anterior cingulate cortex activity as a putative biomarker of treatment response.MethodsThirty-two medication-free patients with depression were treated for 6 weeks with a selective serotonin reuptake inhibitor, escitalopram. Before treatment began, patients underwent an fMRI scan testing response to brief, masked, presentations of facial expression depicting sadness and happiness.ResultsAfter 6 weeks of treatment, there were 20 selective serotonin reuptake inhibitor responders and 12 nonresponders. Increased pretreatment pregenual anterior cingulate cortex activity to sad vs happy faces was observed in responders relative to nonresponders. A leave-one-out analysis suggested that activity in the anterior cingulate cortex was able to predict response status at the level of the individual participant.ConclusionsThe study supports the notion of pregenual anterior cingulate cortex as a promising predictor of antidepressant response.

BackgroundViolence among released prisoners with psychosis is an important public health problem. It is unclear whether treatment in prison can influence criminal behaviour subsequent to release.AimsTo investigate whether treatment in prison can delay time to reoffending.MethodOur sample consisted of 1717 adult prisoners in England and Wales convicted of a serious violent or sexual offence. We used Cox regression to investigate the effects of treatment received in prison on associations between mental illness and time to first reconviction following release.ResultsPrisoners with current symptoms of schizophrenia reoffended quicker following release. Nevertheless, treatment with medication significantly delayed time to violence (18% reduction). Treatment for substance dependence delayed violent and non-violent reoffending among prisoners with drug-induced psychosis.ConclusionsIdentifying prisoners with psychosis and administering treatment in prison have important protective effects against reoffending. Repeated screening with improved accuracy in identification is necessary to prevent cases being missed.Declaration of interestNone.Copyright and usage© The Royal College of Psychiatrists 2015. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) licence.

BackgroundMental health professionals increasingly carry out risk assessments to prevent future violence by their patients. However, there are problems with accuracy and these assessments do not always translate into successful risk management.ObjectivesOur aim was to improve the accuracy of assessment and identify risk factors that are causal to be targeted by clinicians to ensure good risk management. Our objectives were to investigate key risks at the population level, construct new static and dynamic instruments, test validity and construct new models of risk management using Bayesian networks.Methods and resultsWe utilised existing data sets from two national and commissioned a survey to identify risk factors at the population level. We confirmed that certain mental health factors previously thought to convey risk were important in future assessments and excluded others from subsequent parts of the study. Using a first-episode psychosis cohort, we constructed a risk assessment instrument for men and women and showed important sex differences in pathways to violence. We included a 1-year follow-up of patients discharged from medium secure services and validated a previously developed risk assessment guide, the Medium Security Recidivism Assessment Guide (MSRAG). We found that it is essential to combine ratings from static instruments such as the MSRAG with dynamic risk factors. Static levels of risk have important modifying effects on dynamic risk factors for their effects on violence and we further demonstrated this using a sample of released prisoners to construct risk assessment instruments for violence, robbery, drugs and acquisitive convictions. We constructed a preliminary instrument including dynamic risk measures and validated this in a second large data set of released prisoners. Finally, we incorporated findings from the follow-up of psychiatric patients discharged from medium secure services and two samples of released prisoners to construct Bayesian models to guide clinicians in risk management.ConclusionsRisk factors for violence identified at the population level, including paranoid delusions and anxiety disorder, should be integrated in risk assessments together with established high-risk psychiatric morbidity such as substance misuse and antisocial personality disorder. The incorporation of dynamic factors resulted in improved accuracy, especially when combined in assessments using actuarial measures to obtain levels of risk using static factors. It is important to continue developing dynamic risk and protective measures with the aim of identifying factors that are causally related to violence. Only causal factors should be targeted in violence prevention interventions. Bayesian networks show considerable promise in developing software for clinicians to identify targets for intervention in the field. The Bayesian models developed in this programme are at the prototypical stage and require further programmer development into applications for use on tablets. These should be further tested in the field and then compared with structured professional judgement in a randomised controlled trial in terms of their effectiveness in preventing future violence.FundingThe National Institute for Health Research Programme Grants for Applied Research programme.

Psychopathy consists of a constellation of affective-interpersonal features including lack of empathy, callousness, manipulativeness and interpersonal charm, impulsiveness and irresponsibility. Despite its theoretical and predictive value in forensic contexts, the relationships between the psychometric dimensions of psychopathy, including its antisocial features, and the construct's neuropsychological characteristics remain uncertain. In this study, 685 personality-disordered prisoners with histories of serious violent or sexual offenses were assessed for psychopathy before completing a computerized and well-validated assessment of the ability to recognize emotional expressions in the face. Prisoners with more of the affective features of psychopathy, and prisoners with more of its antisocial manifestations, showed relatively poor recognition accuracy of fearfulness and disgust. These relationships were independent and modest but were still evident following correction for demographic features (e.g., ethnicity and socioeconomic status), mental illness (e.g., substance and alcohol misuse), personality disorders (other than antisocial personality disorder) and treatment status. By contrast, the associations between these dimensions of psychopathy and emotion recognition were diminished by controlling for cognitive ability. These findings demonstrate that variability in the ability of high-risk personality-disordered prisoners to recognize emotional expressions in the face-in particular, fear and disgust-reflects both the affective and antisocial aspects of psychopathy, and is moderated by cognitive ability. (PsycINFO Database Record

The prevalence of mental disorders in Greek prisoners was higher than in the general population. Personality disorder was the most common type of mental disorder and the only psychiatric diagnosis related to violent crime. This highlights the need for screening for mental disorders and the need for therapeutic provision within the prison setting.