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

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

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

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

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

<p>WMH Surveys.</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

General mediation model used in analyses.

<p>The figure displays the general mediation model that has been used to estimate effects according to path-diagrammatic conventions. Squares represent variables. <i>D_i</i> is one of the <i>p = 19</i> disorders under consideration, <i>M_j</i> is one of the <i>k = 8</i> mediating variables (disability dimensions), and VAS is the final outcome. Arrows represent regression slope parameters from independent variables to outcomes. The <i>δ</i> parameters stand for the direct effect regression from disorders to the final outcome. The <i>ι</i> parameters indicate the two regression components of the disorder indirect effects as mediated by <i>M</i>: a) <i>p</i> x <i>k</i> regression parameters from <i>D</i> to <i>M</i> (<i>ι_Dij</i>) and b) k regression parameters from <i>M</i> to VAS (<i>ι_M</i>_j). For each disorder the model can be decomposed in two paths: 1) VAS regressed on disorders, and 2) a causal mediation chain of VAS regressed on mediators which in turn are regressed on disorders. The partial indirect effect of a certain disorder <i>D_i</i> through a mediator <i>M_j</i> is <i>ι_Dij</i> x <i>ι_Mj</i>, whereas its total indirect effect is the sum of the <i>k</i> products across mediators (). Total effects for a disorder are the sum of direct and total indirect effect (<i>δ_i</i>+<i>Ι_i</i>). Directionality cannot be assumed as a causal association in our study due to its cross-sectional, observational nature. Also notice that in the general model, the effect of each disorder on each mediator is adjusted by the direct effect of the remaining disorders (thus controlling for comorbidity), while the impact of a disorder on VAS is controlled by the total effects of the other disorders. Disability is thus fully taken into account, even though it is decomposed in subscales. The effects on VAS are also controlled for age, gender, employment status and country.</p