Search

To address the societal harms of violence, many violence prevention interventions have been developed, tested, and implemented in the general population. These have been reported in systematic reviews and meta-analyses, which have typically focused on one type of intervention or outcome. We aimed to provide a comprehensive overview of the current evidence regarding the effectiveness of different psychosocial interventions in reducing all forms of violence toward others. We have conducted an umbrella review of previous meta-analyses using standard approaches and converted findings on effectiveness into odds ratios. We tested for the underlying quality of the meta-analytic evidence by examining heterogeneity, excess statistical significance, prediction intervals, and small study effects. We identified 16 meta-analyses, including nine investigating psychosocial interventions, and five legislative and policy changes. Most meta-analyses reported positive effects of tested interventions. The strongest effects were found for sports-based initiatives, and the weakest for general population programs aimed at early childhood, youth development, and reducing sexual assault perpetration by men. Legislative changes had varying effectiveness. We conclude that simple, scalable, and cost-efficient programs, such as sport-based initiatives, have the clearest empirical support as population-based approaches to violence prevention. [ABSTRACT FROM AUTHOR]

Background Suicide is a leading cause of death in China and accounts for about one-sixth of all suicides worldwide. The objective of this study was to examine the recent distribution of suicide and risk factors for death by suicide. Identifying underlying risk factors could benefit development of evidence-based prevention and intervention programs. Methods and findings We conducted a prospective study, the China Kadoorie Biobank, of 512,715 individuals (41% men, mean age 52 years) from 10 (5 urban, 5 rural) areas which are diverse across China in geographic locations, social economic developmental stages, and prevalence of disease patterns. After the baseline measurements of risk factors during 2004 to 2008, participants were followed up for suicide outcomes including suicide and possible suicide deaths. Risk factors, such as sociodemographic factors and physical and mental health status, were assessed by semistructured interviews and self-report questionnaires. Suicide and possible suicide deaths were identified through linkage to the local death registries using ICD-10 codes. We conducted Cox regression to calculate hazard ratios (HRs) for suicide and for possible suicide in sensitivity analyses. During an average follow-up period of 9.9 years, 520 (101 per 100,000) people died from suicide (51.3% male), and 79.8% of them lived in rural areas. Sociodemographic factors associated with increased suicide risk were male gender (adjusted hazard ratios [aHR] = 1.6 [95% CI 1.4 to 2.0], p < 0.001), older age (1.3 [1.2 to 1.5] by each 10-yr increase, p < 0.001), rural residence (2.6 [2.1 to 3.3], p < 0.001), and single status (1.7 [1.4 to 2.2], p < 0.001). Increased hazards were found for family-related stressful life events (aHR = 1.8 [1.2 to 1.9], p < 0.001) and for major physical illnesses (1.5 [1.3 to 1.9], p < 0.001). There were strong associations of suicide with a history of lifetime mental disorders (aHR = 9.6 [5.9 to 15.6], p < 0.001) and lifetime schizophrenia-spectrum disorders (11.0 [7.1 to 17.0], p < 0.001). Links between suicide risk and depressive disorders (aHR = 2.6 [1.4 to 4.8], p = 0.002) and generalized anxiety disorders (2.6 [1.0 to 7.1], p = 0.056) in the last 12 months, and sleep disorders (1.4 [1.2 to 1.7], p < 0.001) in the past month were also found. All HRs were adjusted for sociodemographic factors including gender, age, residence, single status, education, and income. The associations with possible suicide deaths were mostly similar to those with suicide deaths, although there was no clear link between possible suicide deaths and psychiatric factors such as depression and generalized anxiety disorders. A limitation of the study is that there is likely underreporting of mental disorders due to the use of self-report information for some diagnostic categories. Conclusions In this study, we observed that a range of sociodemographic, lifestyle, stressful life events, physical, and mental health factors were associated with suicide in China. High-risk groups identified were elderly men in rural settings and individuals with mental disorders. These findings could form the basis of targeted approaches to reduce suicide mortality in China., Author(s): Rongqin Yu 1, Yiping Chen 2,3,*, Liming Li 4, Junshi Chen 5, Yu Guo 6, Zheng Bian 6, Jun Lv 4, Canqing Yu 4, Xianmin Xie 7, Dan Huang [...]

Background Intimate partner violence (IPV) against women is associated with a wide range of adverse outcomes. Although mental disorders have been linked to an increased risk of perpetrating IPV against women, the direction and magnitude of the association remain uncertain. In a longitudinal design, we examined the association between mental disorders and IPV perpetrated by men towards women in a population-based sample and used sibling comparisons to control for factors shared by siblings, such as genetic and early family environmental factors. Methods and findings Using Swedish nationwide registries, we identified men from 9 diagnostic groups over 1998-2013, with sample sizes ranging from 9,529 with autism to 88,182 with depressive disorder. We matched individuals by age and sex to general population controls (ranging from 186,017 to 1,719,318 controls), and calculated the hazard ratios of IPV against women. We also estimated the hazard ratios of IPV against women in unaffected full siblings (ranging from 4,818 to 37,885 individuals) compared with the population controls. Afterwards, we compared the hazard ratios for individuals with psychiatric diagnoses with those for siblings using the ratio of hazard ratios (RHR). In sensitivity analyses, we examined the contribution of previous IPV against women and common psychiatric comorbidities, substance use disorders and personality disorders. The average follow-up time across diagnoses ranged from 3.4 to 4.8 years. In comparison to general population controls, all psychiatric diagnoses studied except autism were associated with an increased risk of IPV against women in men, with hazard ratios ranging from 1.5 (95% CI 1.3-1.7) to 7.7 (7.2-8.3) (p-values < 0.001). In sibling analyses, we found that men with depressive disorder, anxiety disorder, alcohol use disorder, drug use disorder, attention deficit hyperactivity disorder, and personality disorders had a higher risk of IPV against women than their unaffected siblings, with RHR values ranging from 1.7 (1.3-2.1) to 4.4 (3.7-5.2) (p-values < 0.001). Sensitivity analyses showed higher risk of IPV against women in men when comorbid substance use disorders and personality disorders were present, compared to risk when these comorbidities were absent. In addition, increased IPV risk was also found in those without previous IPV against women. The absolute rates of IPV against women ranged from 0.1% to 2.1% across diagnoses over 3.4 to 4.8 years. Individuals with alcohol use disorders (1.7%, 1,406/82,731) and drug use disorders (2.1%, 1,216/57,901) had the highest rates. Our analyses were restricted to IPV leading to arrest, suggesting that the applicability of our results may be limited to more severe forms of IPV perpetration. Conclusions Our results indicate that most of the studied mental disorders are associated with an increased risk of perpetrating IPV towards women, and that substance use disorders, as principal or comorbid diagnoses, have the highest absolute and relative risks. The findings support the development of IPV risk identification and prevention services among men with substance use disorders as an approach to reduce the prevalence of IPV., Author(s): Rongqin Yu 1, Alejo J. Nevado-Holgado 1, Yasmina Molero 1, Brian M. D'Onofrio 2,3, Henrik Larsson 3,4, Louise M. Howard 5, Seena Fazel 1,* Introduction Intimate partner violence (IPV) [...]

Objectives: To examine differences in recidivism rates between different prisons using two designs—between-individual and within-individual—to account for confounding factors. Methods: We examined recidivism rates among 37,891 individuals released from 44 Swedish prisons in three security levels, and who were followed from 2006 to 2013. We used longitudinal data from nationwide registers, including all convictions from district courts. First, we applied a between-individual design (Cox proportional hazards regression), comparing reconviction rates between individuals released from prisons within the same security level, while adjusting for a range of individual-level covariates. Second, we applied a within-individual design (stratified Cox proportional hazards regression), comparing rates of reconviction within the same individuals, i.e., we compared rates after release from one prison to the rates in the same individual after release from another prison, thus adjusting for all time-invariant confounders within each individual (e.g. genetics and early environment). We also adjusted for a range of time-varying individual-level covariates. Results: Results showed differences in the hazard of recidivism between different prisons in between-individual analyses, with hazards ranging from 1.22 (1.05–1.43) to 4.99 (2.44–10.21). Results from within-individual analyses, which further adjusted for all time-invariant confounders, showed minimal differences between prisons, with hazards ranging from 0.95 (0.87–1.05) to 1.05 (0.95–1.16). Only small differences were found when violent and non-violent crimes were analyzed separately. Conclusions: The study highlights the importance of research designs that more fully adjust for individual-level confounding factors to avoid over-interpretation of the variability in comparisons across prisons. [ABSTRACT FROM AUTHOR]

Background: Violence risk assessment is a routine part of clinical services in mental health, and in particular secure psychiatric hospitals. The use of prediction models and risk tools can assist clinical decision-making on risk management, including decisions about further assessments, referral, hospitalization and treatment. In recent years, scalable evidence-based tools, such as Forensic Psychiatry and Violent Oxford (FoVOx), have been developed and validated for patients with mental illness. However, their acceptability and utility in clinical settings is not known. Therefore, we conducted a clinical impact study in multiple institutions that provided specialist mental health service. Methods: We followed a two-step mixed-methods design. In phase one, we examined baseline risk factors on 330 psychiatric patients from seven forensic psychiatric institutes in China. In phase two, we conducted semi-structured interviews with 11 clinicians regarding violence risk assessment from ten mental health centres. We compared the FoVOx score on each admission (n = 110) to unstructured clinical risk assessment and used a thematic analysis to assess clinician views on the accuracy and utility of this tool. Results: The median estimated probability of violent reoffending (FoVOx score) within 1 year was 7% (range 1–40%). There was fair agreement (72/99, 73% agreement) on the risk categories between FoVOx and clinicians' assessment on risk categories, and moderate agreement (10/12, 83% agreement) when examining low and high risk categories. In a majority of cases (56/101, 55%), clinicians thought the FoVOx score was an accurate representation of the violent risk of an individual patient. Clinicians suggested some additional clinical, social and criminal risk factors should be considered during any comprehensive assessment. In addition, FoVOx was considered to be helpful in assisting clinical decision-making and individual risk assessment. Ten out of 11 clinicians reported that FoVOx was easy to use, eight out of 11 was practical, and all clinicians would consider using it in the future. Conclusions: Clinicians found that violence risk assessment could be improved by using a simple, scalable tool, and that FoVOx was feasible and practical to use. [ABSTRACT FROM AUTHOR]

Background: Ox ford M ental I llness and S uicide tool (OxMIS) is a brief, scalable, freely available, structured risk assessment tool to assess suicide risk in patients with severe mental illness (schizophrenia-spectrum disorders or bipolar disorder). OxMIS requires further external validation, but a lack of large-scale cohorts with relevant variables makes this challenging. Electronic health records provide possible data sources for external validation of risk prediction tools. However, they contain large amounts of information within free-text that is not readily extractable. In this study, we examined the feasibility of identifying suicide predictors needed to validate OxMIS in routinely collected electronic health records. Methods: In study 1, we manually reviewed electronic health records of 57 patients with severe mental illness to calculate OxMIS risk scores. In study 2, we examined the feasibility of using natural language processing to scale up this process. We used anonymized free-text documents from the Clinical Record Interactive Search database to train a named entity recognition model, a machine learning technique which recognizes concepts in free-text. The model identified eight concepts relevant for suicide risk assessment: medication (antidepressant/antipsychotic treatment), violence, education, self-harm, benefits receipt, drug/alcohol use disorder, suicide, and psychiatric admission. We assessed model performance in terms of precision (similar to positive predictive value), recall (similar to sensitivity) and F1 statistic (an overall performance measure). Results: In study 1, we estimated suicide risk for all patients using the OxMIS calculator, giving a range of 12 month risk estimates from 0.1-3.4%. For 13 out of 17 predictors, there was no missing information in electronic health records. For the remaining 4 predictors missingness ranged from 7-26%; to account for these missing variables, it was possible for OxMIS to estimate suicide risk using a range of scores. In study 2, the named entity recognition model had an overall precision of 0.77, recall of 0.90 and F1 score of 0.83. The concept with the best precision and recall was medication (precision 0.84, recall 0.96) and the weakest were suicide (precision 0.37), and drug/alcohol use disorder (recall 0.61). Conclusions: It is feasible to estimate suicide risk with the OxMIS tool using predictors identified in routine clinical records. Predictors could be extracted using natural language processing. However, electronic health records differ from other data sources, particularly for family history variables, which creates methodological challenges. [ABSTRACT FROM AUTHOR]