Forest plot of all 35 selected studies: prevalence of violence estimates (boxes) with 95% confidence limit (bars); pooled prevalence is reported as diamond.

<p>Forest plot of all 35 selected studies: prevalence of violence estimates (boxes) with 95% confidence limit (bars); pooled prevalence is reported as diamond.</p

Summary information of the studies included in meta-analysis.

<p>^cInformation was obtained from the author.</p><p>Summary information of the studies included in meta-analysis.</p

Additional information of the studies included in meta-analysis.

<p>Additional information of the studies included in meta-analysis.</p

Comparing forensic and non-forensic women with schizophrenia spectrum disorders: a European study

Studies about violence by women with severe mental disorders are rare. The aim of this paper is to analyse the sample of women diagnosed with Schizophrenia Spectrum Disorders (SSD) from the EU-VIORMED study who had offended violently and were admitted to forensic facilities (cases), and compare them to women with SSD who never exhibited violent behaviour (controls). Cases and controls matched for age and diagnosis were compared for sociodemographic, clinical, neuropsychological, and treatment-related characteristics using a standardised assessment. When compared to 36 controls, the 26 cases were significantly older, with longer duration of illness, had fewer years of education, were less likely to have children, and were more likely to have a comorbid personality disorder. Cases were less functionally impaired and scored lower on cognitive domains. There were no differences between the groups in exposure to childhood or adult violence, but a greater proportion of cases reported more frequently being witness to and victims of violence and more frequently reported being beaten, kicked, or punched. Results suggest that the emergence of violent behaviour in women with SSD might be shaped by various factors including violent victimisation, personality factors, soft cognitive impairment and perhaps as a result a more extended duration of illness.</p

Characteristics of WMH samples and percent of respondents reporting lifetime traumatic events.

<p>Characteristics of WMH samples and percent of respondents reporting lifetime traumatic events.</p

The potentially traumatic events assessed in the World Mental Health Surveys.

<p>The potentially traumatic events assessed in the World Mental Health Surveys.</p

AOO of Drug Use among Those Reporting Any Use by Country

<p>Note: Where lines are not presented for an individual country, either there was no assessment of the AOO of that drug, or fewer than 30 persons reported having used the drug (see <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0050141#pmed-0050141-t002" target="_blank">Tables 2</a> and <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0050141#pmed-0050141-t005" target="_blank">5</a>).</p

Distribution of the WHODAS dimension scores by income level. The WMH Surveys.

<p>Distribution of the WHODAS dimension scores by income level. The WMH Surveys.</p

Direct and indirect effects (via WHODAS dimensions) of common chronic conditions on perceived health VAS, overall sample.

<p>WMH Surveys.</p

Suicidal behavior assessed with interactions between DSM-IV PTSD and individual traumatic events¹.

<p>*Significant at the .05 level, two-sided test.</p>1<p>Multiviate models included interaction terms between DSM-IV PTSD and each trauma event. Only interaction terms are shown in the table, while the main effects are still controlled for. Assessed in Part II sample due to having Part II controls. Some countries were missing part of the trauma variables and were coded “No” for those variables: Combat, Exposure to War, Refugee were all coded “No” for India and Brazil, and Natural Disaster also coded “No” for Brazil. For Israel, the entire sample is coded “Yes” for exposure to war with the age of onset set to the age they moved to Israel. Controls for all models included person-year, country, demographic factors (age, sex, time-varying education, time-varying marriage), interactions between life course (3 dichotomous dummies representing early, middle, and later years in the person's life) and demographic variables, parent psychopathology, and childhood adversities (additional details available upon request).</p