Effects of antipsychotic treatment on cardio-cerebrovascular related mortality in schizophrenia: A subanalysis of a systematic review and meta-analysis with meta-regression of moderators.

To further explore the role of different antipsychotic treatments for cardio-cerebrovascular mortality, we performed several subgroup, sensitivity and meta-regression analyses based on a large previous meta-analysis focusing on cohort studies assessing mortality relative risk (RR) for cardio-cerebrovascular disorders in people with schizophrenia, comparing antipsychotic treatment versus no antipsychotic. Quality assessment through the Newcastle-Ottawa Scale (NOS) and publication bias was measured. We meta-analyzed 53 different studies (schizophrenia patients: n = 2,513,359; controls: n = 360,504,484) to highlight the differential effects of antipsychotic treatment regimens on cardio-cerebrovascular-related mortality in incident and prevalent samples of patients with schizophrenia. We found first generation antipsychotics (FGA) to be associated with higher mortality in incident samples of schizophrenia (oral FGA [RR=2.20, 95 %CI=1.29-3.77, k = 1] and any FGA [RR=1.70, 95 %CI=1.20-2.41, k = 1]). Conversely, second generation antipsychotics (SGAs) and clozapine were associated with reduced cardio-cerebrovascular-related mortality, in prevalent samples of schizophrenia. Subgroup analyses with NOS score ≥7 (higher quality) demonstrated a significantly increased cardio-cerebrovascular disorder-related mortality, among those exposed to FGAs vs SGAs. Meta-regression analyses demonstrated a larger association between antipsychotics and decreased risk of mortality with longer follow-up, recent study year, and higher number of adjustment variables. Overall, this subanalysis of a systematic review contributes to the evolving understanding of the complex role of antipsychotic treatment for cardio-cerebrovascular mortality in schizophrenia, paving the way for more targeted interventions and improved patient outcomes.

Patients' perspective on the therapeutic relationship and session quality: the central role of alliance.

In this study, we examined how four components of the therapeutic relationship-working alliance, real relationship, and positive and negative affective reactions of the patient toward their therapist-relate to each other and to the psychotherapy session outcome, from the patient's point of view. Our simple comprised 700 adult patients in individual psychotherapy who were recruited and participated online. They underwent a baseline evaluation of their most recent therapy session, which encompassed a series of validated self-report measures focused on specific elements of the therapeutic relationship. The results revealed that, from the patient's perspective, working alliance, real relationship, and positive affective reactions toward the therapist were positively correlated with session outcome, while negative affective reactions were negatively correlated. All components predicted session outcome when simultaneously included in a regression model. Collectively, these four components accounted for 30% of the variance in session outcome. Factor analysis revealed four distinct factors, underlying perceptions of the therapeutic relationship. Notably, the bond dimension of the alliance was sufficiently different from the task and goal dimensions, warranting consideration as a distinct construct. These findings, although cross-sectional, lay the groundwork for a more nuanced investigation of multiple dimensions of the therapeutic relationship.

All-cause mortality risk in long-acting injectable versus oral antipsychotics in schizophrenia: a systematic review and meta-analysis.

Patients with schizophrenia receiving antipsychotic treatment present lower mortality rates than those who do not. However, the non-adherence rate is high, which can be partially addressed using long-acting injectable (LAI) antipsychotics. The impact of LAI treatments on all-cause mortality compared to oral antipsychotics remains unclear. To fill that gap, a random effects meta-analysis was conducted to analyze the odds ratio (OR) of all-cause, suicidal, and non-suicidal mortality among patients taking LAI antipsychotics compared to oral antipsychotics (PROSPERO:CRD42023391352). Individual and pooled LAI antipsychotics were analyzed against pooled oral antipsychotics. Sensitivity analyses were performed for study design, setting, and industry sponsorship. Meta-regressions were conducted for gender, age, antipsychotic dose, and race. Seventeen articles, total sample 12,042 patients (N = 5795 oral, N = 6247 LAI) were included. Lower risk of all-cause mortality for patients receiving LAI antipsychotics vs receiving oral antipsychotics was found (OR = 0.79; 95%CI = 0.66-0.95). Statistical significance was maintained when only studies comparing the same LAI and oral antipsychotic were included (OR = 0.79; 95%CI = 0.66-0.95; p = <0.01), as well as for non-suicidal mortality (OR = 0.77: 95%CI = 0.63-0.94; p = 0.01), but not for suicidal mortality (OR = 0.86; 95%CI = 0.59-1.26; p = 0.44). Mortality reduction was more pronounced for LAI antipsychotics in first-episode psychosis (FEP) (OR = 0.79; 95%CI = 0.66-0.96) compared to chronic psychosis. No individual LAI reported statistically significant differences against all pooled oral antipsychotics. LAI antipsychotics are associated with a lower risk of all-cause and non-suicidal mortality in individuals with schizophrenia compared to oral antipsychotics. Better adherence to the medication and health services may explain this difference. Whenever possible, the use of LAIs should be considered from the FEP.

Do Cognitive Subtypes Exist in People at Clinical High Risk for Psychosis? Results From the EU-GEI Study.

BACKGROUND AND HYPOTHESIS: Cognition has been associated with socio-occupational functioning in individuals at Clinical High Risk for Psychosis (CHR-P). The present study hypothesized that clustering CHR-P participants based on cognitive data could reveal clinically meaningful subtypes. STUDY DESIGN: A cohort of 291 CHR-P subjects was recruited through the multicentre EU-GEI high-risk study. We explored whether an underlying cluster structure was present in the cognition data. Clustering of cognition data was performed using k-means clustering and density-based spatial clustering of applications with noise. Cognitive subtypes were validated by comparing differences in functioning, psychosis symptoms, transition outcome, and grey matter volume between clusters. Network analysis was used to further examine relationships between cognition scores and clinical symptoms. STUDY RESULTS: No underlying cluster structure was found in the cognitive data. K-means clustering produced "spared" and "impaired" cognition clusters similar to those reported in previous studies. However, these clusters were not associated with differences in functioning, symptomatology, outcome, or grey matter volume. Network analysis identified cognition and symptoms/functioning measures that formed separate subnetworks of associations. CONCLUSIONS: Stratifying patients according to cognitive performance has the potential to inform clinical care. However, we did not find evidence of cognitive clusters in this CHR-P sample. We suggest that care needs to be taken in inferring the existence of distinct cognitive subtypes from unsupervised learning studies. Future research in CHR-P samples could explore the existence of cognitive subtypes across a wider range of cognitive domains.

Short- and long-term neuropsychiatric outcomes in long COVID in South Korea and Japan.

We investigated whether SARS-CoV-2 infection is associated with short- and long-term neuropsychiatric sequelae. We used population-based cohorts from the Korean nationwide cohort (discovery; n = 10,027,506) and the Japanese claims-based cohort (validation; n = 12,218,680) to estimate the short-term (<30 days) and long-term (≥30 days) risks of neuropsychiatric outcomes after SARS-CoV-2 infection compared with general population groups or external comparators (people with another respiratory infection). Using exposure-driven propensity score matching, we found that both the short- and long-term risks of developing neuropsychiatric sequelae were elevated in the discovery cohort compared with the general population and those with another respiratory infection. A range of conditions including Guillain-Barré syndrome, cognitive deficit, insomnia, anxiety disorder, encephalitis, ischaemic stroke and mood disorder exhibited a pronounced increase in long-term risk. Factors such as mild severity of COVID-19, increased vaccination against COVID-19 and heterologous vaccination were associated with reduced long-term risk of adverse neuropsychiatric outcomes. The time attenuation effect was the strongest during the first six months after SARS-CoV-2 infection, and this risk remained statistically significant for up to one year in Korea but beyond one year in Japan. The associations observed were replicated in the validation cohort. Our findings contribute to the growing evidence base on long COVID by considering ethnic diversity.

Use of Machine Learning Algorithms Based on Text, Audio, and Video Data in the Prediction of Anxiety and Posttraumatic Stress in General and Clinical Populations: A Systematic Review.

Research in machine learning (ML) algorithms using natural behavior (i.e., text, audio, and video data) suggests that these techniques could contribute to personalization in psychology and psychiatry. However, a systematic review of the current state of the art is missing. Moreover, individual studies often target ML experts who may overlook potential clinical implications of their findings. In a narrative accessible to mental health professionals, we present a systematic review conducted in 5 psychology and 2 computer science databases. We included 128 studies that assessed the predictive power of ML algorithms using text, audio, and/or video data in the prediction of anxiety and posttraumatic stress disorder. Most studies (n = 87) were aimed at predicting anxiety, while the remainder (n = 41) focused on posttraumatic stress disorder. They were mostly published since 2019 in computer science journals and tested algorithms using text (n = 72) as opposed to audio or video. Studies focused mainly on general populations (n = 92) and less on laboratory experiments (n = 23) or clinical populations (n = 13). Methodological quality varied, as did reported metrics of the predictive power, hampering comparison across studies. Two-thirds of studies, which focused on both disorders, reported acceptable to very good predictive power (including high-quality studies only). The results of 33 studies were uninterpretable, mainly due to missing information. Research into ML algorithms using natural behavior is in its infancy but shows potential to contribute to diagnostics of mental disorders, such as anxiety and posttraumatic stress disorder, in the future if standardization of methods, reporting of results, and research in clinical populations are improved.

Dynamic and Transdiagnostic Risk Calculator Based on Natural Language Processing for the Prediction of Psychosis in Secondary Mental Health Care: Development and Internal-External Validation Cohort Study.

BACKGROUND: Automatic transdiagnostic risk calculators can improve the detection of individuals at risk of psychosis. However, they rely on assessment at a single point in time and can be refined with dynamic modeling techniques that account for changes in risk over time. METHODS: We included 158,139 patients (5007 events) who received a first index diagnosis of a nonorganic and nonpsychotic mental disorder within electronic health records from the South London and Maudsley National Health Service Foundation Trust between January 1, 2008, and October 8, 2021. A dynamic Cox landmark model was developed to estimate the 2-year risk of developing psychosis according to the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis) statement. The dynamic model included 24 predictors extracted at 9 landmark points (baseline, 0, 6, 12, 24, 30, 36, 42, and 48 months): 3 demographic, 1 clinical, and 20 natural language processing-based symptom and substance use predictors. Performance was compared with a static Cox regression model with all predictors assessed at baseline only and indexed via discrimination (C-index), calibration (calibration plots), and potential clinical utility (decision curves) in internal-external validation. RESULTS: The dynamic model improved discrimination performance from baseline compared with the static model (dynamic: C-index = 0.9; static: C-index = 0.87) and the final landmark point (dynamic: C-index = 0.79; static: C-index = 0.76). The dynamic model was also significantly better calibrated (calibration slope = 0.97-1.1) than the static model at later landmark points (≥24 months). Net benefit was higher for the dynamic than for the static model at later landmark points (≥24 months). CONCLUSIONS: These findings suggest that dynamic prediction models can improve the detection of individuals at risk for psychosis in secondary mental health care settings.

Exploring causal mechanisms of psychosis risk.

Robust epidemiological evidence of risk and protective factors for psychosis is essential to inform preventive interventions. Previous evidence syntheses have classified these risk and protective factors according to their strength of association with psychosis. In this critical review we appraise the distinct and overlapping mechanisms of 25 key environmental risk factors for psychosis, and link these to mechanistic pathways that may contribute to neurochemical alterations hypothesised to underlie psychotic symptoms. We then discuss the implications of our findings for future research, specifically considering interactions between factors, exploring universal and subgroup-specific factors, improving understanding of temporality and risk dynamics, standardising operationalisation and measurement of risk and protective factors, and developing preventive interventions targeting risk and protective factors.

On the Road to Individualizing Pharmacotherapy for Adolescents and Adults with Schizophrenia - Results from an Expert Consensus Following the Delphi Method.

Introduction: Schizophrenia is a severe mental illness that usually begins in late adolescence or early adulthood. Current pharmacological treatments, while acceptably effective for many patients, are rarely clinically tailored or individualized. The lack of sufficient etiopathological knowledge of the disease, together with overall comparable effect sizes for efficacy between available antipsychotics and the absence of clinically actionable biomarkers, has hindered the advance of individualized medicine in the treatment of schizophrenia. Nevertheless, some degree of stratification based on clinical markers could guide treatment choices and help clinicians move toward individualized psychiatry. To this end, a panel of experts met to formally discuss the current approach to individualized treatment in schizophrenia and to define how treatment individualization could help improve clinical outcomes. Methods: A task force of seven experts iteratively developed, evaluated, and refined questionnaire items, which were then evaluated using the Delphi method. Descriptive statistics were used to summarize and rank expert responses. Expert discussion, informed by the results of a scoping review on personalizing the pharmacologic treatment of adults and adolescents with schizophrenia, ultimately generated recommendations to guide individualized pharmacologic treatment in this population. Results: There was substantial agreement among the expert group members, resulting in the following recommendations: 1) individualization of treatment requires consideration of the patient's diagnosis, clinical presentation, comorbidities, previous treatment response, drug tolerability, adherence patterns, and social factors; 2) patient preferences should be considered in a shared decision-making approach; 3) identified barriers to personalized care that need to be overcome include the lack of actionable biomarkers and mechanistic similarities between available treatments, but digital tools should be increasingly used to enhance individualized treatment. Conclusion: Individualized care can help provide effective, tailored treatments based on an individual's clinical characteristics, disease trajectory, family and social environment, and goals and preferences.

Major Depression as a Disorder of the Narrative Self: A Qualitative Study.

INTRODUCTION: Abnormal self-experiences are a common feature of major depression despite their absence from current diagnostic manuals. Current diagnostic criteria leave us with an impoverished conception of depressive disorders, and they fail to exploit the diverse experiential alterations that might be useful for understanding and diagnosing patients, and last but not least for explaining the aetiology of these disorders. Although some phenomenological descriptions of abnormal self-experiences in major depression are available, further research is needed to validate these through detailed clinical interviews. METHODS: To characterize these phenomena in more detail and to verify and consolidate previous accounts, we conducted a qualitative study using the Consensual Qualitative Research method. RESULTS: Our findings identified three categories of abnormal self-experiences: (1) impossibility to project oneself forward, (2) not recognizing one's self, and (3) losing control on one's self. CONCLUSION: Before delving into these results, we briefly described how the self is conceptualized in phenomenological psychopathology and explored in the literature on the self-experience in major depression. After discussing our results in the light of recent and contemporary phenomenological literature, we suggest that the inability to recognize otherness as part of oneself - which is the core of depressive experiences - ends in specific symptoms of depersonalization that differ from schizophrenic ones. We conclude that the self-experience, and in particular narrative identity, is central to the development and maintenance of depression.

Individualized prediction models in ADHD: a systematic review and meta-regression.

There have been increasing efforts to develop prediction models supporting personalised detection, prediction, or treatment of ADHD. We overviewed the current status of prediction science in ADHD by: (1) systematically reviewing and appraising available prediction models; (2) quantitatively assessing factors impacting the performance of published models. We did a PRISMA/CHARMS/TRIPOD-compliant systematic review (PROSPERO: CRD42023387502), searching, until 20/12/2023, studies reporting internally and/or externally validated diagnostic/prognostic/treatment-response prediction models in ADHD. Using meta-regressions, we explored the impact of factors affecting the area under the curve (AUC) of the models. We assessed the study risk of bias with the Prediction Model Risk of Bias Assessment Tool (PROBAST). From 7764 identified records, 100 prediction models were included (88% diagnostic, 5% prognostic, and 7% treatment-response). Of these, 96% and 7% were internally and externally validated, respectively. None was implemented in clinical practice. Only 8% of the models were deemed at low risk of bias; 67% were considered at high risk of bias. Clinical, neuroimaging, and cognitive predictors were used in 35%, 31%, and 27% of the studies, respectively. The performance of ADHD prediction models was increased in those models including, compared to those models not including, clinical predictors (ß = 6.54, p = 0.007). Type of validation, age range, type of model, number of predictors, study quality, and other type of predictors did not alter the AUC. Several prediction models have been developed to support the diagnosis of ADHD. However, efforts to predict outcomes or treatment response have been limited, and none of the available models is ready for implementation into clinical practice. The use of clinical predictors, which may be combined with other type of predictors, seems to improve the performance of the models. A new generation of research should address these gaps by conducting high quality, replicable, and externally validated models, followed by implementation research.

Assessing the patient's affective perception of their psychotherapist: validation of the in-Session Patient Affective Reactions Questionnaire.

Background: Psychotherapists need effective tools to monitor changes in the patient's affective perception of the therapist and the therapeutic relationship during sessions to tailor therapeutic interventions and improve treatment outcomes. This study aims to evaluate the factor structure, reliability, and validity of the in-Session Patient Affective Reactions Questionnaire (SPARQ), a concise self-report measure designed for practical application in real-world psychotherapy settings. Methods: Validation data was gathered from (N = 700) adult patients in individual psychotherapy. These patients completed the SPARQ in conjunction with additional measures capturing sociodemographic details, characteristics of therapeutic interventions, individual personality traits, mental health symptom severity, elements of the therapeutic relationship, and session outcomes. This comprehensive approach was employed to assess the construct and criterion-related validity of the SPARQ. Results: The SPARQ has a two-factor structure: Positive Affect (k = 4, ω total = .87) and Negative Affect (k = 4, ω total = .75). Bifactor confirmatory factor analysis (CFA) yielded the following fit indices: X2[df] = 2.53, CFI = .99; TLI = .98; RMSEA = .05; and SRMR = .02. Multi-group CFAs demonstrated measurement invariance (i) across patients who attended psychotherapy sessions in person versus in remote mode, and (ii) across patients with and without psychiatric diagnoses confirmed metric invariance. Furthermore, the SPARQ showed meaningful correlations with concurrently administered measures. Discussion: The SPARQ proves to be a valuable instrument in clinical, training, and research contexts, adept at capturing patients' session-level affective responses towards their therapist and perceptions of the therapeutic alliance. Comprehensive descriptive statistics and a range of score precision indices have been reported, intended to serve as benchmarks for future research.

Sexually transmitted infections, sexual life and risk behaviours of people living with schizophrenia: systematic review and meta-analysis.

BACKGROUND: Sexually transmitted infections (STIs), along with sexual health and behaviour, have received little attention in schizophrenia patients. AIMS: To systematically review and meta-analytically characterise the prevalence of STIs and sexual risk behaviours among schizophrenia patients. METHOD: Web of Science, PubMed, BIOSIS, KCI-Korean Journal Database, MEDLINE, Russian Science Citation Index, SciELO and Cochrane Central Register were systematically searched from inception to 6 July 2023. Studies reporting on the prevalence or odds ratio of any STI or any outcome related to sexual risk behaviours among schizophrenia samples were included. PRISMA/MOOSE-compliant (CRD42023443602) random-effects meta-analyses were used for the selected outcomes. Q-statistics, I2 index, sensitivity analyses and meta-regressions were used. Study quality and publication bias were assessed. RESULTS: Forty-eight studies (N = 2 459 456) reporting on STI prevalence (including 15 allowing for calculation of an odds ratio) and 33 studies (N = 4255) reporting on sexual risk behaviours were included. Schizophrenia samples showed a high prevalence of STIs and higher risks of HIV (odds ratio = 2.11; 95% CI 1.23-3.63), hepatitis C virus (HCV, odds ratio = 4.54; 95% CI 2.15-961) and hepatitis B virus (HBV; odds ratio = 2.42; 95% CI 1.95-3.01) infections than healthy controls. HIV prevalence was higher in Africa compared with other continents and in in-patient (rather than out-patient) settings. Finally, 37.7% (95% CI 31.5-44.4%) of patients were sexually active; 35.0% (95% CI 6.6-59.3%) reported consistent condom use, and 55.3% (95% CI 25.0-82.4%) maintained unprotected sexual relationships. CONCLUSIONS: Schizophrenia patients have high prevalence of STIs, with several-fold increased risks of HIV, HBV and HCV infection compared with the general population. Sexual health must be considered as an integral component of care.

A transdiagnostic prodrome for severe mental disorders: an electronic health record study.

Effective prevention of severe mental disorders (SMD), including non-psychotic unipolar mood disorders (UMD), non-psychotic bipolar mood disorders (BMD), and psychotic disorders (PSY), rely on accurate knowledge of the duration, first presentation, time course and transdiagnosticity of their prodromal stages. Here we present a retrospective, real-world, cohort study using electronic health records, adhering to RECORD guidelines. Natural language processing algorithms were used to extract monthly occurrences of 65 prodromal features (symptoms and substance use), grouped into eight prodromal clusters. The duration, first presentation, and transdiagnosticity of the prodrome were compared between SMD groups with one-way ANOVA, Cohen's f and d. The time course (mean occurrences) of prodromal clusters was compared between SMD groups with linear mixed-effects models. 26,975 individuals diagnosed with ICD-10 SMD were followed up for up to 12 years (UMD = 13,422; BMD = 2506; PSY = 11,047; median[IQR] age 39.8[23.7] years; 55% female; 52% white). The duration of the UMD prodrome (18[36] months) was shorter than BMD (26[35], d = 0.21) and PSY (24[38], d = 0.18). Most individuals presented with multiple first prodromal clusters, with the most common being non-specific ('other'; 88% UMD, 85% BMD, 78% PSY). The only first prodromal cluster that showed a medium-sized difference between the three SMD groups was positive symptoms (f = 0.30). Time course analysis showed an increase in prodromal cluster occurrences approaching SMD onset. Feature occurrence across the prodromal period showed small/negligible differences between SMD groups, suggesting that most features are transdiagnostic, except for positive symptoms (e.g. paranoia, f = 0.40). Taken together, our findings show minimal differences in the duration and first presentation of the SMD prodromes as recorded in secondary mental health care. All the prodromal clusters intensified as individuals approached SMD onset, and all the prodromal features other than positive symptoms are transdiagnostic. These results support proposals to develop transdiagnostic preventive services for affective and psychotic disorders detected in secondary mental healthcare.

The lived experience of mental disorders in adolescents: a bottom-up review co-designed, co-conducted and co-written by experts by experience and academics.

We provide here the first bottom-up review of the lived experience of mental disorders in adolescents co-designed, co-conducted and co-written by experts by experience and academics. We screened first-person accounts within and outside the medical field, and discussed them in collaborative workshops involving numerous experts by experience - representing different genders, ethnic and cultural backgrounds, and continents - and their family members and carers. Subsequently, the material was enriched by phenomenologically informed perspectives and shared with all collaborators. The inner subjective experience of adolescents is described for mood disorders, psychotic disorders, attention-deficit/hyperactivity disorder, autism spectrum disorders, anxiety disorders, eating disorders, externalizing disorders, and self-harm behaviors. The recollection of individuals' past histories also indexes the prodromal (often transdiagnostic) features predating the psychiatric diagnosis. The experience of adolescents with mental disorders in the wider society is described with respect to their family, their school and peers, and the social and cultural context. Furthermore, their lived experience of mental health care is described with respect to receiving a diagnosis of mental disorder, accessing mental health support, receiving psychopharmacological treatment, receiving psychotherapy, experiencing peer support and mental health activism, and achieving recovery. These findings can impact clinical practice, research, and the whole society. We hope that this co-designed, co-conducted and co-written journey can help us maintain our commitment to protecting adolescents' fragile mental health, and can help them develop into a healthy, fulfilling and contributing adult life.

Impact of air pollution and climate change on mental health outcomes: an umbrella review of global evidence.

The impact of air pollution and climate change on mental health has recently raised strong concerns. However, a comprehensive overview analyzing the existing evidence while addressing relevant biases is lacking. This umbrella review systematically searched the PubMed/Medline, Scopus and PsycINFO databases (up to June 26, 2023) for any systematic review with meta-analysis investigating the association of air pollution or climate change with mental health outcomes. We used the R metaumbrella package to calculate and stratify the credibility of the evidence according to criteria (i.e., convincing, highly suggestive, suggestive, or weak) that address several biases, complemented by sensitivity analyses. We included 32 systematic reviews with meta-analysis that examined 284 individual studies and 237 associations of exposures to air pollution or climate change hazards and mental health outcomes. Most associations (n=195, 82.3%) involved air pollution, while the rest (n=42, 17.7%) regarded climate change hazards (mostly focusing on temperature: n=35, 14.8%). Mental health outcomes in most associations (n=185, 78.1%) involved mental disorders, followed by suicidal behavior (n=29, 12.4%), access to mental health care services (n=9, 3.7%), mental disorders-related symptomatology (n=8, 3.3%), and multiple categories together (n=6, 2.5%). Twelve associations (5.0%) achieved convincing (class I) or highly suggestive (class II) evidence. Regarding exposures to air pollution, there was convincing (class I) evidence for the association between long-term exposure to solvents and a higher incidence of dementia or cognitive impairment (odds ratio, OR=1.139), and highly suggestive (class II) evidence for the association between long-term exposure to some pollutants and higher risk for cognitive disorders (higher incidence of dementia with high vs. low levels of carbon monoxide, CO: OR=1.587; higher incidence of vascular dementia per 1 µg/m3 increase of nitrogen oxides, NOx: hazard ratio, HR=1.004). There was also highly suggestive (class II) evidence for the association between exposure to airborne particulate matter with diameter ≤10 µm (PM10) during the second trimester of pregnancy and the incidence of post-partum depression (OR=1.023 per 1 µg/m3 increase); and for the association between short-term exposure to sulfur dioxide (SO2) and schizophrenia relapse (risk ratio, RR=1.005 and 1.004 per 1 µg/m3 increase, respectively 5 and 7 days after exposure). Regarding climate change hazards, there was highly suggestive (class II) evidence for the association between short-term exposure to increased temperature and suicide- or mental disorders-related mortality (RR=1.024), suicidal behavior (RR=1.012), and hospital access (i.e., hospitalization or emergency department visits) due to suicidal behavior or mental disorders (RR=1.011) or mental disorders only (RR=1.009) (RR values per 1°C increase). There was also highly suggestive (class II) evidence for the association between short-term exposure to increased apparent temperature (i.e., the temperature equivalent perceived by humans) and suicidal behavior (RR=1.01 per 1°C increase). Finally, there was highly suggestive (class II) evidence for the association between the temporal proximity of cyclone exposure and severity of symptoms of post-traumatic stress disorder (r=0.275). Although most of the above associations were small in magnitude, they extend to the entire world population, and are therefore likely to have a substantial impact. This umbrella review classifies and quantifies for the first time the global negative impacts that air pollution and climate change can exert on mental health, identifying evidence-based targets that can inform future research and population health actions.

Effects of Benzodiazepine Exposure on Real-World Clinical Outcomes in Individuals at Clinical High Risk for Psychosis.

BACKGROUND AND HYPOTHESIS: Animal models indicate GABAergic dysfunction in the development of psychosis, and that benzodiazepine (BDZ) exposure can prevent the emergence of psychosis-relevant phenotypes. However, whether BDZ exposure influences real-world clinical outcomes in individuals at clinical high risk for psychosis (CHR-P) is unknown. STUDY DESIGN: This observational cohort study used electronic health record data from CHR-P individuals to investigate whether BDZ exposure (including hypnotics, eg, zopiclone) reduces the risk of developing psychosis and adverse clinical outcomes. Cox proportional-hazards models were employed in both the whole-unmatched sample, and a propensity score matched (PSM) subsample. STUDY RESULTS: 567 CHR-P individuals (306 male, mean[±SD] age = 22.3[±4.9] years) were included after data cleaning. The BDZ-exposed (n = 105) and BDZ-unexposed (n = 462) groups differed on several demographic and clinical characteristics, including psychotic symptom severity. In the whole-unmatched sample, BDZ exposure was associated with increased risk of transition to psychosis (HR = 1.61; 95% CI: 1.03-2.52; P = .037), psychiatric hospital admission (HR = 1.93; 95% CI: 1.13-3.29; P = .017), home visit (HR = 1.64; 95% CI: 1.18-2.28; P = .004), and Accident and Emergency department attendance (HR = 1.88; 95% CI: 1.31-2.72; P < .001). However, after controlling for confounding-by-indication through PSM, BDZ exposure did not modulate the risk of any outcomes (all P > .05). In an analysis restricted to antipsychotic-naïve individuals, BDZ exposure reduced the risk of transition to psychosis numerically, although this was not statistically significant (HR = 0.59; 95% CI: 0.32-1.08; P = .089). CONCLUSIONS: BDZ exposure in CHR-P individuals was not associated with a reduction in the risk of psychosis transition or adverse clinical outcomes. Results in the whole-unmatched sample suggest BDZ prescription may be more likely in CHR-P individuals with higher symptom severity.

Effects of diazepam on hippocampal blood flow in people at clinical high risk for psychosis.

Elevated hippocampal perfusion has been observed in people at clinical high risk for psychosis (CHR-P). Preclinical evidence suggests that hippocampal hyperactivity is central to the pathophysiology of psychosis, and that peripubertal treatment with diazepam can prevent the development of psychosis-relevant phenotypes. The present experimental medicine study examined whether diazepam can normalize hippocampal perfusion in CHR-P individuals. Using a randomized, double-blind, placebo-controlled, crossover design, 24 CHR-P individuals were assessed with magnetic resonance imaging (MRI) on two occasions, once following a single oral dose of diazepam (5 mg) and once following placebo. Regional cerebral blood flow (rCBF) was measured using 3D pseudo-continuous arterial spin labeling and sampled in native space using participant-specific hippocampus and subfield masks (CA1, subiculum, CA4/dentate gyrus). Twenty-two healthy controls (HC) were scanned using the same MRI acquisition sequence, but without administration of diazepam or placebo. Mixed-design ANCOVAs and linear mixed-effects models were used to examine the effects of group (CHR-P placebo/diazepam vs. HC) and condition (CHR-P diazepam vs. placebo) on rCBF in the hippocampus as a whole and by subfield. Under the placebo condition, CHR-P individuals (mean [±SD] age: 24.1 [±4.8] years, 15 F) showed significantly elevated rCBF compared to HC (mean [±SD] age: 26.5 [±5.1] years, 11 F) in the hippocampus (F(1,41) = 24.7, pFDR < 0.001) and across its subfields (all pFDR < 0.001). Following diazepam, rCBF in the hippocampus (and subfields, all pFDR < 0.001) was significantly reduced (t(69) = -5.1, pFDR < 0.001) and normalized to HC levels (F(1,41) = 0.4, pFDR = 0.204). In conclusion, diazepam normalized hippocampal hyperperfusion in CHR-P individuals, consistent with evidence implicating medial temporal GABAergic dysfunction in increased vulnerability for psychosis.

Effect of long-acting antipsychotic treatment on psychiatric hospitalization rate in early psychosis patients: a naturalistic study.

Background: The effectiveness of long-acting injectable (LAI) antipsychotics in preventing relapses of first-episode psychosis is currently debated. Objectives: The study aimed to investigate the number of psychiatric hospitalizations comparing the LAI cohort versus the oral cohort during different phases of the illness, pre-LAI treatment, during LAI treatment, and after LAI treatment. Design: A naturalistic study was conducted on two independent cohorts of early psychosis patients receiving treatment from a specific early intervention service. The first cohort comprised 228 patients who received LAIs, while the second cohort comprised 667 patients who had never received LAIs. Methods: This study was designed as a longitudinal observational study conducted within a naturalistic clinical setting in two cohorts of early psychosis patients. Repeated series ANCOVA (ANCOVA-r) was used to study the number of hospitalizations in the different study periods (T1 = from the date of the first psychiatric record to the beginning of the mirror period; T2 = the mirror period; T3 = from the LAI implementation to the LAI discontinuation; and T4 = from the LAI discontinuation to the end). In all cases, discontinuation of LAI involved the return to oral treatment. In all, 35 patients had not T4 as they were still on LAI treatment at the time of database closing (September 2020), and their data were not included in the analysis of the effect of the LAI discontinuation. Results: The patients in the LAI cohort were younger, more frequently males, presented more schizophrenia diagnoses, and had a higher number of hospitalizations (2.50 ± 2.61 versus 1.19 ± 1.69; p < 0.001) than the oral cohort. The number of hospitalizations at the end of the follow-up was higher in the LAI cohort [0.20 (standard deviation (SD)) = 0.79] versus 0.45 [SD = 0.45 (SD = 1.13); F(23.90), p < 0.001]. However, after the introduction of LAIs, the differences in hospitalization rates between the two cohorts became less pronounced. Once LAI treatment was ceased, the hospitalization rate increased again. Conclusion: In our study, early psychosis patients receiving LAIs experienced a greater decrease in hospitalizations after introducing the LAI treatment than those treated solely with oral medication. These findings support using LAIs as a viable strategy for preventing rehospitalization and improving the overall course of treatment for individuals with early psychosis.