Aims.: To evaluate treatment decision-making capacity (DMC) to consent to psychiatric treatment in involuntarily committed patients and to further investigate possible associations with clinical and socio-demographic characteristics of patients.Methods.: 131 involuntarily hospitalised patients were recruited in three university hospitals. Mental capacity to consent to treatment was measured with the MacArthur Competence Assessment Tool for Treatment (MacCAT-T); psychiatric symptoms severity (Brief Psychiatric Rating Scale, BPRS-E) and cognitive functioning (Mini Mental State Examination, MMSE) were also assessed.Results.: Mental capacity ratings for the 131 involuntarily hospitalised patients showed that patients affected by bipolar disorders (BD) scored generally better than those affected by schizophrenia spectrum disorders (SSD) in MacCAT-T appreciation (p< 0.05) and reasoning (p< 0.01). Positive symptoms were associated with poorer capacity to appreciate (r= −0.24;p< 0.01) and reason (r= −0.27;p< 0.01) about one's own treatment. Negative symptoms were associated with poorer understanding of treatment (r= −0.23;p< 0.01). Poorer cognitive functioning, as measured by MMSE, negatively affected MacCAT-T understanding in patients affected by SSD, but not in those affected by BD (SSDr= 0.37;p< 0.01; BDr= −0.01;p= 0.9). Poorer MacCAT-T reasoning was associated with more manic symptoms in the BD group of patients but not in the SSD group (BDr= −0.32;p< 0.05; SSDr= 0.03;p= 0.8). Twenty-two per cent (n= 29) of the 131 recruited patients showed high treatment DMC as defined by having scored higher than 75% ofunderstanding, appreciating and reasoningMacCAT-T subscales maximum sores and 2 atexpressing a choice. The remaining involuntarily hospitalised patients where considered to have low treatment DMC. Chi-squared disclosed that 32% of BD patients had high treatment DMC compared with 9% of SSD patients (p< 0.001).Conclusions.: Treatment DMC can be routinely assessed in non-consensual psychiatric settings by the MacCAT-T, as is the case of other clinical variables. Such approach can lead to the identification of patients with high treatment DMC, thus drawing attention to possible dichotomy between legal and clinical status.

We conducted a systematic, qualitative review of risk prediction models designed and tested for depression, bipolar disorder, generalized anxiety disorder, posttraumatic stress disorder, and psychotic disorders. Our aim was to understand the current state of research on risk prediction models for these 5 disorders and thus future directions as our field moves toward embracing prediction and prevention. Systematic searches of the entire MEDLINE electronic database were conducted independently by 2 of the authors (from 1960 through 2013) in July 2014 using defined search criteria. Search terms included risk prediction, predictive model, or prediction model combined with depression, bipolar, manic depressive, generalized anxiety, posttraumatic, PTSD, schizophrenia, or psychosis. We identified 268 articles based on the search terms and 3 criteria: published in English, provided empirical data (as opposed to review articles), and presented results pertaining to developing or validating a risk prediction model in which the outcome was the diagnosis of 1 of the 5 aforementioned mental illnesses. We selected 43 original research reports as a final set of articles to be qualitatively reviewed. The 2 independent reviewers abstracted 3 types of data (sample characteristics, variables included in the model, and reported model statistics) and reached consensus regarding any discrepant abstracted information. Twelve reports described models developed for prediction of major depressive disorder, 1 for bipolar disorder, 2 for generalized anxiety disorder, 4 for posttraumatic stress disorder, and 24 for psychotic disorders. Most studies reported on sensitivity, specificity, positive predictive value, negative predictive value, and area under the (receiver operating characteristic) curve. Recent studies demonstrate the feasibility of developing risk prediction models for psychiatric disorders (especially psychotic disorders). The field must now advance by (1) conducting more large-scale, longitudinal studies pertaining to depression, bipolar disorder, anxiety disorders, and other psychiatric illnesses; (2) replicating and carrying out external validations of proposed models; (3) further testing potential selective and indicated preventive interventions; and (4) evaluating effectiveness of such interventions in the context of risk stratification using risk prediction models.

Aims: Aim of the current study is to investigate the associations between daily levels of air pollutants (particulate matter, ozone, carbon monoxide, nitrogen dioxide) and daily admissions for mental disorders to the emergency department of two general hospitals in Umbria region (Italy).Methods: We collected data about daily admissions to psychiatric emergency services of two general hospitals, air pollutants' levels and meteorological data for the time period 1 January 2015 until 31 December 2016. We assessed the impact of an increase in air pollutants on the number of daily admissions using a time-series econometric framework.Results: A total of 1860 emergency department admissions for mental disorders were identified. We observed a statistically significant impact of ozone levels on daily admissions. The estimated coefficient of O₃is statistically significant at the 1% level. All other pollutants were not significantly associated with the number of daily admissions.Conclusions: Short-term exposure to ozone may be associated with increased psychiatric emergency services admissions. Findings add to previous literature on existing evidence for air pollution to have an impact on mental health. Ozone may be considered a potential environmental risk factor for impaired mental health.