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Background:Community sentences are widely used in many countries, often comprising the majority of criminal justice sanctions. Psychiatric disorders are highly prevalent in community-sentenced populations and are thus potential targets for treatment interventions designed to reduce reoffending. We examined the association between psychiatric disorders and reoffending in a national cohort of individuals given community sentences in Sweden, with use of a sibling control design to account for unmeasured familial confounding. Methods:We did a longitudinal cohort study of 82 415 individuals given community sentences between Nov 1, 1991, and Dec 31, 2013, in Sweden using data from population-based registers. We calculated hazard ratios (HRs) for any reoffending and violent reoffending with Cox regression models. We compared community-sentenced siblings with and without psychiatric disorders to control for potential familial confounding. Additionally, we calculated population attributable fractions to assess the contribution of psychiatric disorders to reoffending behaviours. The primary outcomes of the study were any (general) reoffending and violent reoffending. Findings:Between Nov 1, 1991, and Dec 31, 2013, those given community sentences who were diagnosed with any psychiatric disorder had an increased reoffending risk in men (adjusted HR 1·59, 95% CI 1·56–1·63 for any reoffending; 1·60, 1·54–1·66 for violent reoffending) and women (1·71, 1·61–1·82 for any reoffending; 2·19, 1·88–2·54 for violent reoffending). Risk estimates remained elevated after adjustment for familial confounding. Schizophrenia spectrum disorders, personality disorders, and substance use disorders had stronger associations with violent reoffending than did other psychiatric disorders. Assuming causality, the adjusted population attributable risk of psychiatric disorders on violent reoffending was 8·3% (95% CI 6·6–10·0) in the first 2 years of community follow-up in men and 30·9% (22·7–39·0) in women. Interpretation:Psychiatric disorders were associated with an increased risk of any reoffending and violent reoffending in the community-sentenced population. The magnitude of the association between psychiatric disorders and reoffending varied by individual diagnosis. Substance use disorders had the highest absolute and relative risks. Most of the increased risk for any reoffending in individuals with psychiatric disorders could be attributed to comorbid substance misuse. Given their high prevalence, substance use disorders should be the focus of treatment programmes in community-sentenced populations.

QuestionEffective prevention of suicide requires a comprehensive understanding of risk factors.Study selection and analysisFive databases were systematically searched to identify psychological autopsy studies (published up to February 2022) that reported on risk factors for suicide mortality among adults in the general population. Effect sizes were pooled as odds ratios (ORs) using random-effects models for each risk factor examined in at least three independent samples.FindingsA total of 37 case–control studies from 23 countries were included, providing data on 40 risk factors in 5633 cases and 7101 controls. The magnitude of effect sizes varied substantially both between and within risk factor domains. Clinical factors had the strongest associations with suicide, including any mental disorder (OR=13.1, 95% CI 9.9 to 17.4) and a history of self-harm (OR=10.1, 95% CI 6.6 to 15.6). By comparison, effect sizes were smaller for other domains relating to sociodemographic status, family history, and adverse life events (OR range 2–5).ConclusionsA wide range of predisposing and precipitating factors are associated with suicide among adults in the general population, but with clear differences in their relative strength.PROSPERO registration numberCRD42021232878.

Oxford Mental Illness and Suicide tool (OxMIS) is a standardised, scalable, and transparent instrument for suicide risk assessment in people with severe mental illness (SMI) based on 17 sociodemographic, criminal history, familial, and clinical risk factors. However, alongside most prediction models in psychiatry, external validations are currently lacking. We utilised a Finnish population sample of all persons diagnosed by mental health services with SMI (schizophrenia-spectrum and bipolar disorders) between 1996 and 2017 (n = 137,112). To evaluate the performance of OxMIS, we initially calculated the predicted 12-month suicide risk for each individual by weighting risk factors by effect sizes reported in the original OxMIS prediction model and converted to a probability. This probability was then used to assess the discrimination and calibration of the OxMIS model in this external sample. Within a year of assessment, 1.1% of people with SMI (n = 1475) had died by suicide. The overall discrimination of the tool was good, with an area under the curve of 0.70 (95% confidence interval: 0.69–0.71). The model initially overestimated suicide risks in those with elevated predicted risks of >5% over 12 months (Harrell’s Emax = 0.114), which applied to 1.3% (n = 1780) of the cohort. However, when we used a 5% maximum predicted suicide risk threshold as is recommended clinically, the calibration was excellent (ICI = 0.002; Emax = 0.005). Validating clinical prediction tools using routinely collected data can address research gaps in prediction psychiatry and is a necessary step to translating such models into clinical practice.

Background Although there is robust evidence for several factors which may precipitate self-harm, the contributions of different physical injuries are largely unknown. Objective To examine whether specific physical injuries are associated with risks of self-harm in people with psychiatric disorders. Methods By using population and secondary care registers, we identified all people born in Finland (1955–2000) and Sweden (1948–1993) with schizophrenia-spectrum disorder (n=136 182), bipolar disorder (n=68 437) or depression (n=461 071). Falls, transport-related injury, traumatic brain injury and injury from interpersonal assault were identified within these subsamples. We used conditional logistic regression models adjusted for age and calendar month to compare self-harm risk in the week after each injury to earlier weekly control periods, which allowed us to account for unmeasured confounders, including genetics and early environments. Findings A total of 249 210 individuals had been diagnosed with a psychiatric disorder and a physical injury during the follow-up. The absolute risk of self-harm after a physical injury ranged between transport-related injury and injury from interpersonal assault (averaging 17.4–37.0 events per 10 000 person-weeks). Risk of self-harm increased by a factor of two to three (adjusted OR: 2.0–2.9) in the week following a physical injury, as compared with earlier, unexposed periods for the same individuals. Conclusions Physical injuries are important proximal risk factors for self-harm in people with psychiatric disorders. Clinical implications Mechanisms underlying the associations could provide treatment targets. When treating patients with psychiatric illnesses, emergency and trauma medical services should actively work in liaison with psychiatric services to implement self-harm prevention strategies.

Gambling problems are often associated with homelessness and linked to elevated psychiatric morbidity and homelessness chronicity. We performed a systematic review and meta-analysis on prevalence rates of problem gambling (PG) and gambling disorder (GD) in homeless people. Following PRISMA guidelines, we searched databases Medline, Embase and PsycINFO from inception of databases to 4th may 2021. We included studies reporting prevalence estimates on clinical gambling problems in representative samples of homeless people based on standardized diagnostics. Risk of bias was assessed. A random effects meta-analysis was performed, and subgroup analyses based on methodological characteristics of primary studies were conducted. We identified eight studies from five countries, reporting information on 1938 participants. Prevalence rates of clinically significant PG and GD ranged from 11.3 to 31.3%. There was evidence for substantial heterogeneity with I2 = 86% (95% CI 63–97%). A subgroup of four low risk of bias studies displayed a significantly lower results ranging from 11.3 to 23.6%. Additionally, high rates of subclinical problem gambling were reported (11.6–56.4%). At least one in ten homeless persons experiences clinically significant PG or GD. Social support and health care services for the homeless should address this problem by implementing models for early detection and treatment.

Introduction Risk assessment is integral to forensic psychiatry. Previous work has highlighted the benefits of using scalable and evidence-based actuarial risk tools developed within forensic populations, such as the online Forensic Psychiatry and Violence Oxford (FoVOx) violence risk assessment tool. We examined the feasibility of using FoVOx in a Swedish forensic cohort and tested whether adding modifiable (dynamic) factors would increase its useability to clinicians. Methods We completed FoVOx assessments on all patients discharged from forensic psychiatric hospitals in Stockholm County, Sweden, between 2012 and 2017 and investigated recidivism rates. In addition, interviews were conducted with the clinicians responsible for each patient on the perceived accuracy, usefulness, and impact of FoVOx, which was examined using thematic analysis. Results Ninety-five discharges from forensic psychiatric hospitals were followed up. The median FoVOx score was a 7% likelihood of violent reoffending in two years after discharge. Six discharged patients (6%) were confirmed as violent recidivists using official records with a similar distribution of FoVOx risk categories as the rest of the sample. FoVOx was considered accurate by clinicians in more than half of cases, who suggested that modifiable risk factors could be added to increase acceptability. All clinicians thought that FoVOx was useful, and in 20% of discharges, it would have materially altered patient care. Overall, FoVOx was thought to impact decision-making and risk management, was practical to use, and could be completed without reference to written case material. Conclusion Completing FoVOx in forensic psychiatric hospitals can complement current approaches to clinical decision-making on violence risk assessment and management.

We conducted a series of tests on the FORensic oUtcome Measure (FORUM), a novel tool for measuring outcomes in forensic mental health services, which consists of complementary patient-reported (FORUM-P) and clinician-reported instruments (FORUM-C). Inpatients and outpatients at a UK forensic psychiatric regional service completed the FORUM-P and members of their clinical teams completed the FORUM-C. Patients and clinicians also provided qualitative feedback on the instruments. We assessed test-retest and inter-rater reliability in standard ways. Sixty-two patients participated with a mean age of 41.0 years (standard deviation 11.3). Thirty-five clinicians provided information about these patients. For internal consistency, Cronbach’s alpha for FORUM-P was 0.87 (95% confidence interval 0.80–0.93) and for FORUM-C was 0.93 (95% confidence interval 0.91–0.96). For test-retest reliability, weighted kappa for FORUM-P was 0.44 (95% confidence interval 0.24–0.63) and for FORUM-C was 0.78 (95% confidence interval 0.73–0.85). For inter-rater reliability, Spearman correlation coefficient for overall FORUM-C score between the first rating by clinician 1 and clinician 2 was 0.47 (95% confidence interval 0.18–0.69). For comprehensiveness, comprehensibility, and relevance FORUM-P and FORUM-C were both rated as good. FORUM-P and FORUM-C provide a novel, robust set of complementary instruments with promising psychometric properties for monitoring outcomes in forensic mental health.

Over 11 million people are imprisoned worldwide at any given time. Despite methodological challenges, high levels of psychiatric morbidity in people in prison have been documented in many countries, including low- and middle-income countries. Research has consistently shown high levels of major depression, psychosis, posttraumatic stress disorder, substance use disorders and, to a lesser extent, developmental disorders among individuals in prison. Existing evidence also reported high prevalence rates for the co-occurrence of several mental health and substance use problems. Suicide and self-harm are more common in people in prison than community-based persons of similar age and gender. Women, juveniles, and people in prison aged 55 years and older present with higher rates of many disorders than other persons in prison settings. The prison environment is stressful and people suffering from psychiatric disorders may have difficulties coping with the many stressors of the prison regime (violence, victimization, solitary confinement, etc.). Equivalence of healthcare inside and outside prison, independence from the justice system, respect for confidentiality, and informed consent are core ethical principles of practicing psychiatry in prison settings. Evidence-based best practice should be the goal for the psychiatric treatment (including screening and assessment, medication, and psychotherapy) of incarcerated people. Importantly, the transition from prison to the community is associated with poor outcomes and psychiatry in prison settings is therefore not sufficient without follow-up care after release. Research gaps about the mental health problems of people in prison are outlined.

Background Cost-effectiveness analysis needs to be considered when introducing new tools and treatments to clinical services. The number of new assessment tools in mental health has rapidly expanded, including suicide risk assessment. Such suicide-based assessments, when linked to preventative interventions, are integral to high-quality mental health care for people with severe mental illness (SMI). We examined the cost implications of implementing Oxford Mental Illness and Suicide (OxMIS), an evidence-based, scalable suicide risk assessment tool that provides probabilistic estimates of suicide risk over 12 months for people with SMI in England. Methods We developed a decision analytic model using secondary data to estimate the potential cost-effectiveness of incorporating OxMIS into clinical decision-making in secondary care as compared to usual care. Cost-effectiveness was measured in terms of costs per quality-adjusted life years (QALYs) gained. Uncertainty was addressed with deterministic and probabilistic sensitivity analysis. Results Conducting suicide risk assessment with OxMIS was potentially cheaper than clinical risk assessment alone by £250 (95% confidence interval, −786;31) to £599 (−1,321;−156) (in 2020–2021 prices) per person with SMI and associated with a small increase in quality of life (0.01 [−0.03;0.05] to 0.01 QALY, [−0.04;0.07]). The estimated incremental cost-effectiveness ratio of implementing OxMIS was cost saving. Using probabilistic sensitivity analysis, 99.96% of 10,000 simulations remained cost saving. Conclusion Cost-effectiveness analysis can be conducted on risk prediction models. Implementing one such model that focuses on suicide risk in a high-risk population can lead to cost savings and improved health outcomes, especially if explicitly linked to preventative treatments.

Oxford Mental Illness and Suicide tool (OxMIS) is a standardised, scalable, and transparent instrument for suicide risk assessment in people with severe mental illness (SMI) based on 17 sociodemographic, criminal history, familial, and clinical risk factors. However, alongside most prediction models in psychiatry, external validations are currently lacking. We utilized a Finnish population sample of all persons diagnosed by mental health services with SMI (schizophrenia-spectrum and bipolar disorders) between 1996 and 2017 (n = 137 112). To evaluate the performance of OxMIS, we initially calculated the predicted 12-month suicide risk for each individual by weighting risk factors by effect sizes reported in the original OxMIS prediction model and converted to a probability. This probability was then used to assess the discrimination and calibration of the OxMIS model in this external sample. Within a year of assessment, 1.1% of people with SMI (n = 1475) had died by suicide. The overall discrimination of the tool was good, with an area under the curve of 0.70 (95% confidence interval: 0.69–0.71). The model initially overestimated suicide risks in those with elevated predicted risks of > 5% over 12 months (Harrell's Emax = 0.114), which applied to 1.3% (n = 1780) of the cohort. However, when we used a 5% maximum predicted suicide risk threshold as is recommended clinically, the calibration was excellent (ICI = 0.002; Emax = 0.005). Validating clinical prediction tools using routinely collected data can address research gaps in prediction psychiatry and is a necessary step to translating such models into clinical practice.

Background: Using other central nervous system (CNS) medications in combination with selective serotonin reuptake inhibitor (SSRI) treatment is common. Despite this, there is limited evidence on the impact on suicidal behavior of combining specific medications. We aim to provide evidence on signals for suicidal behavior risk when initiating CNS drugs during and outside of SSRI treatment. Materials and methods: Using a linkage of Swedish national registers, we identified a national cohort of SSRI users aged 6–59 years residing in Sweden 2006–2013. We used a two-stage Bayesian Poisson model to estimate the incidence rate ratio (IRR) of suicidal behavior in periods up to 90 days before and after a CNS drug initiation during SSRI treatment, while accounting for multiple testing. For comparison, and to assess whether there were interactions between SSRIs and other CNS drugs, we also estimated the IRR of initiating the CNS drug without SSRI treatment. Results: We identified 53 common CNS drugs initiated during SSRI treatment, dispensed to 262,721 individuals. We found 20 CNS drugs with statistically significant IRRs. Of these, two showed a greater risk of suicidal behavior after versus before initiating the CNS drug (alprazolam, IRR = 1.39; flunitrazepam, IRR = 1.83). We found several novel signals of drugs that were statistically significantly associated with a reduction in the suicidal behavior risk. We did not find evidence of harmful interactions between SSRIs and the selected CNS drugs. Conclusion: Several of the detected signals for reduced risk correspond to drugs where there is previous evidence of benefit for antidepressant augmentation (e.g., olanzapine, quetiapine, lithium, buspirone, and mirtazapine). Novel signals of reduced suicidal behavior risk, including for lamotrigine, valproic acid, risperidone, and melatonin, warrant further investigation.

Purpose: We aimed to externally validate the Oxford Risk of Recidivism (OxRec) tool to estimate 1- and 2-year risk of violent reoffending in people released from prison in England.Methods: We identified people released from prison using administrative data shared between official prison and police services. We extracted information on criminal history, clinical and sociodemographic risk predictors, and outcomes. The outcomes were violent reoffending at 1 and 2 years after release from prison, identified using official police data. Predictive ability was examined using measures of calibration (calibration statistics and plots) and discrimination (area under the receiver operating characteristic curve [AUC]; sensitivity, specificity, positive and negative predictive values [PPV; NPV]) for predetermined risk thresholds. Recalibration of the model was conducted when necessary.Results: In total, 1,770 individuals (median age = 33 [IQR 27–40]; 92% were male) were identified as recently released from prison. 31% (n = 550) and 43% (n = 765) reoffended within 1 and 2 years, respectively. Simple validation of the original model found a systematic underestimation of the probability of reoffending. However, after recalibration, OxRec was associated with AUCs of 0.71 (95% CI: 0.69-0.74) for 1-year and 0.71 (0.68-0.74) for 2-year follow-up. At a pre-specified threshold of 40% for 2-year violent reoffending risk, sensitivity was 77% (95% CI: 74%-80%), specificity 54% (51%-58%), PPV 56% (53%-59%) and NPV 76% (73%-79%). In addition, in the revised model, measures of calibration were good (calibration in the large was null for both time points). Conclusions: External validations of risk assessment tools for reoffending with adequate sample sizes can be conducted using linked data between prison and police services, and may require model recalibration before implementation. In this validation, OxRec had good performance on measures of discrimination and calibration. It can be considered as part of approaches to improve decision-making about risk of serious offending and the allocation of resources in the criminal justice system.

Background β-blockers are widely used for treating cardiac conditions and are suggested for the treatment of anxiety and aggression, although research is conflicting and limited by methodological problems. In addition, β-blockers have been associated with precipitating other psychiatric disorders and suicidal behaviour, but findings are mixed. We aimed to examine associations between β-blockers and psychiatric and behavioural outcomes in a large population-based cohort in Sweden. Methods and findings We conducted a population-based longitudinal cohort study using Swedish nationwide high-quality healthcare, mortality, and crime registers. We included 1,400,766 individuals aged 15 years or older who had collected β-blocker prescriptions and followed them for 8 years between 2006 and 2013. We linked register data on dispensed β-blocker prescriptions with main outcomes, hospitalisations for psychiatric disorders (not including self-injurious behaviour or suicide attempts), suicidal behaviour (including deaths from suicide), and charges of violent crime. We applied within-individual Cox proportional hazards regression to compare periods on treatment with periods off treatment within each individual in order to reduce possible confounding by indication, as this model inherently adjusts for all stable confounders (e.g., genetics and health history). We also adjusted for age as a time-varying covariate. In further analyses, we adjusted by stated indications, prevalent users, cardiac severity, psychiatric and crime history, individual β-blockers, β-blocker selectivity and solubility, and use of other medications. In the cohort, 86.8% (n = 1,215,247) were 50 years and over, and 52.2% (n = 731,322) were women. During the study period, 6.9% (n = 96,801) of the β-blocker users were hospitalised for a psychiatric disorder, 0.7% (n = 9,960) presented with suicidal behaviour, and 0.7% (n = 9,405) were charged with a violent crime. There was heterogeneity in the direction of results; within-individual analyses showed that periods of β-blocker treatment were associated with reduced hazards of psychiatric hospitalisations (hazard ratio [HR]: 0.92, 95% confidence interval [CI]: 0.91 to 0.93, p < 0.001), charges of violent crime (HR: 0.87, 95% CI: 0.81 to 0.93, p < 0.001), and increased hazards of suicidal behaviour (HR: 1.08, 95% CI: 1.02 to 1.15, p = 0.012). After stratifying by diagnosis, reduced associations with psychiatric hospitalisations during β-blocker treatment were mainly driven by lower hospitalisation rates due to depressive (HR: 0.92, 95% CI: 0.89 to 0.96, p < 0.001) and psychotic disorders (HR: 0.89, 95% CI: 0.85 to 0.93, p < 0.001). Reduced associations with violent charges remained in most sensitivity analyses, while associations with psychiatric hospitalisations and suicidal behaviour were inconsistent. Limitations include that the within-individual model does not account for confounders that could change during treatment, unless measured and adjusted for in the model. Conclusions In this population-wide study, we found no consistent links between β-blockers and psychiatric outcomes. However, β-blockers were associated with reductions in violence, which remained in sensitivity analyses. The use of β-blockers to manage aggression and violence could be investigated further.

There is concern that selective serotonin reuptake inhibitor (SSRI) treatment may increase the risk of suicide attempts or deaths, particularly among children and adolescents. However, debate remains regarding the nature of the relationship. Using nationwide Swedish registers, we identified all individuals aged 6–59 years with an incident SSRI dispensation (N = 538,577) from 2006 to 2013. To account for selection into treatment, we used a within-individual design to compare the risk of suicide attempts or deaths (suicidal behaviour) in time periods before and after SSRI-treatment initiation. Within-individual incidence rate ratios (IRRs) of suicidal behaviour were estimated. The 30 days before SSRI-treatment initiation was associated with the highest risk of suicidal behaviour compared with the 30 days 1 year before SSRI initiation (IRR = 7.35, 95% CI 6.60–8.18). Compared with the 30 days before SSRI initiation, treatment periods after initiation had a reduced risk—the IRR in the 30 days after initiation was 0.62 (95% CI 0.58–0.65). The risk then declined over treatment time. These patterns were similar across age strata, and when stratifying on history of suicide attempts. Initiation with escitalopram was associated with the greatest risk reduction, though CIs for the IRRs of the different SSRI types were overlapping. The results do not suggest that SSRI-treatment increases the risk for suicidal behaviour in either youths or adults; rather, it may reduce the risk. Further research with different study designs and in different populations is warranted.

Assessment of suicide risk in individuals with severe mental illness is currently inconsistent, and based on clinical decision-making with or without tools developed for other purposes. We aimed to develop and validate a predictive model for suicide using data from linked population-based registers in individuals with severe mental illness. A national cohort of 75,158 Swedish individuals aged 15–65 with a diagnosis of severe mental illness (schizophrenia-spectrum disorders, and bipolar disorder) with 574,018 clinical patient episodes between 2001 and 2008, split into development (58,771 patients, 494 suicides) and external validation (16,387 patients, 139 suicides) samples. A multivariable derivation model was developed to determine the strength of pre-specified routinely collected socio-demographic and clinical risk factors, and then tested in external validation. We measured discrimination and calibration for prediction of suicide at 1 year using specified risk cut-offs. A 17-item clinical risk prediction model for suicide was developed and showed moderately good measures of discrimination (c-index 0.71) and calibration. For risk of suicide at 1 year, using a pre-specified 1% cut-off, sensitivity was 55% (95% confidence interval [CI] 47–63%) and specificity was 75% (95% CI 74–75%). Positive and negative predictive values were 2% and 99%, respectively. The model was used to generate a simple freely available web-based probability-based risk calculator (Oxford Mental Illness and Suicide tool or OxMIS) without categorical cut-offs. A scalable prediction score for suicide in individuals with severe mental illness is feasible. If validated in other samples and linked to effective interventions, using a probability score may assist clinical decision-making.

Scalable and transparent methods for risk assessment are increasingly required in criminal justice to inform decisions about sentencing, release, parole, and probation. However, few such approaches exist and their validation in external settings is typically lacking. A total national sample of all offenders (9072 released from prisoners and 6329 individuals on probation) from 2011–2012 in the Netherlands were followed up for violent and any reoffending over 2 years. The sample was mostly male (n = 574 [6%] were female prisoners and n = 784 [12%] were female probationers), and median ages were 30 in the prison sample and 34 in those on probation. Predictors for a scalable risk assessment tool (OxRec) were extracted from a routinely collected dataset used by criminal justice agencies, and outcomes from official criminal registers. OxRec’s predictive performance in terms of discrimination and calibration was tested. Reoffending rates in the Dutch prisoner cohort were 16% for 2-year violent reoffending and 44% for 2-year any reoffending, with lower rates in the probation sample. Discrimination as measured by the c-index was moderate, at 0.68 (95% CI: 0.66–0.70) for 2-year violent reoffending in prisoners and between 0.65 and 0.68 for other outcomes and the probation sample. The model required recalibration, after which calibration performance was adequate (e.g. calibration in the large was 1.0 for all scenarios). A recalibrated model for OxRec can be used in the Netherlands for individuals released from prison and individuals on probation to stratify their risk of future violent and any reoffending. The approach that we outline can be considered for external validations of criminal justice and clinical risk models.

Introduction: Adolescents detained within the criminal justice system are distinguished by complex health problems, health risk behaviours, and high rates of premature death. We undertook a global synthesis of the evidence regarding the health of this population.Theory/Methods: We searched Embase, PsycINFO, ERIC, PubMed, Web of Science, CINCH, Global Health, the Cochrane Database of Systematic Reviews, the Campbell Library, the National Criminal Justice Reference System Abstract Database, and Google Scholar for peer-reviewed journal articles that a) reported the prevalence of at least one health outcome in adolescents (aged

Background: Current approaches to stratify psychiatric patients into groups based on violence risk are limited by inconsistency, variable accuracy, and unscalability. Methods: Based on a national cohort of 75 158 Swedish individuals aged 15–65 with a diagnosis of severe mental illness (schizophrenic-spectrum and bipolar disorders) with 574 018 patient episodes, we developed predictive models for violent offending through linkage of population-based registers. First, a derivation model was developed to determine strength of pre-specified criminal history, socio-demographic, and clinical risk factors, and tested it in external validation. We measured discrimination and calibration for prediction of violent offending at 1 year using specified risk cut-offs. Results: A 16 item model was developed from criminal history, socio-demographic and clinical risk factors, which are mostly routinely collected. In external validation, the model showed good measures of discrimination (c-index 0.89) and calibration. For risk of violent offending at 1 year, using a 5% cut off, sensitivity was 64% and specificity was 94%. Positive and negative predictive values were 11% and 99%, respectively. The model was used to generate a simple web-based risk calculator (OxMIV). Discussion: We have developed a prediction score in a national cohort of patients with psychosis that can be used as an adjunct to decision making in clinical practice by identifying those who are at low risk of violent offending.

Objective To synthesize evidence on the prevalence of mental disorders in adolescents in juvenile detention and correctional facilities, and examine sources of heterogeneity between studies. Method We searched electronic databases and relevant reference lists to identify surveys published from 1966 to October 2019 that reported on the prevalence of mental disorders in unselected populations of detained adolescents. We extracted data on the prevalence of a range of mental disorders (psychotic illnesses, major depression, attention-deficit/hyperactivity disorder [ADHD], conduct disorder, and posttraumatic stress disorder [PTSD]) along with predetermined study characteristics from the eligible studies. We report analyses separately for male and female adolescents, and synthesize findings using random-effects models. Potential sources of heterogeneity are examined by metaregression and subgroup analyses. Results We identified 47 studies from 19 countries on 28,033 male and 4,754 female adolescents. The mean age of adolescents assessed was 16 years (range 10-19 years). In male adolescents, 2.7% (95% confidence interval [CI] 2.0%-3.4%) had a diagnosis of psychotic illness, 10.1% (8.1%-12.2%) major depression, 17.3% (13.9%-20.7%) ADHD, 61.7% (55.4%-67.9%) conduct disorder, and 8.6% (6.4%-10.7%) PTSD. In female adolescents, 2.9% (2.4%-3.5%) had a psychotic illness, 25.8% (20.3%-31.3%) major depression, 17.5% (12.1%-22.9%) ADHD, 59.0% (44.9%-73.1%) conduct disorder, and 18.2% (13.1%-23.2%) PTSD. Metaregression found higher prevalences of ADHD and conduct disorder in newer investigations. Female adolescents had higher prevalences of major depression and PTSD than male adolescents. Conclusion Consideration should be given to reviewing whether healthcare services in juvenile detention can meet these levels of psychiatric morbidity.