The prenatal and early-life periods pose a crucial neurodevelopmental window whereby disruptions to the intestinal microbiota and the developing brain may have adverse impacts. As antibiotics affect the human intestinal microbiome, it follows that early-life antibiotic exposure may be associated with later-life psychiatric or neurocognitive outcomes.

To explore the association between early-life (in utero and early childhood (age 0–2 years)) antibiotic exposure and the subsequent risk of psychiatric and neurocognitive outcomes.

A search was conducted using Medline, PsychINFO and Excerpta Medica databases on 20 November 2023. Risk of bias was assessed using the Newcastle-Ottawa scale, and certainty was assessed using the grading of recommendations, assessment, development and evaluation (GRADE) certainty assessment.

Thirty studies were included (n = 7 047 853 participants). Associations were observed between in utero antibiotic exposure and later development of autism spectrum disorder (ASD) (odds ratio 1.09, 95% CI: 1.02–1.16) and attention-deficit hyperactivity disorder (ADHD) (odds ratio 1.19, 95% CI: 1.11–1.27) and early-childhood exposure and later development of ASD (odds ratio 1.19, 95% CI: 1.01–1.40), ADHD (odds ratio 1.33, 95% CI: 1.20–1.48) and major depressive disorder (MDD) (odds ratio 1.29, 95% CI: 1.04–1.60). However, studies that used sibling control groups showed no significant association between early-life exposure and ASD or ADHD. No studies in MDD used sibling controls. Using the GRADE certainty assessment, all meta-analyses but one were rated very low certainty, largely owing to methodological and statistical heterogeneity.

While there was weak evidence for associations between antibiotic use in early-life and later neurodevelopmental outcomes, these were attenuated in sibling-controlled subgroup analyses. Thus, associations may be explained by genetic and familial confounding, and studies failing to utilise sibling-control groups must be interpreted with caution. PROSPERO ID: CRD42022304128

Involuntary admissions to psychiatric hospitals are on the rise. If patients at elevated risk of involuntary admission could be identified, prevention may be possible. Our aim was to develop and validate a prediction model for involuntary admission of patients receiving care within a psychiatric service system using machine learning trained on routine clinical data from electronic health records (EHRs).

EHR data from all adult patients who had been in contact with the Psychiatric Services of the Central Denmark Region between 2013 and 2021 were retrieved. We derived 694 patient predictors (covering e.g. diagnoses, medication, and coercive measures) and 1134 predictors from free text using term frequency-inverse document frequency and sentence transformers. At every voluntary inpatient discharge (prediction time), without an involuntary admission in the 2 years prior, we predicted involuntary admission 180 days ahead. XGBoost and elastic net models were trained on 85% of the dataset. The models with the highest area under the receiver operating characteristic curve (AUROC) were tested on the remaining 15% of the data.

The model was trained on 50 634 voluntary inpatient discharges among 17 968 patients. The cohort comprised of 1672 voluntary inpatient discharges followed by an involuntary admission. The best XGBoost and elastic net model from the training phase obtained an AUROC of 0.84 and 0.83, respectively, in the test phase.

A machine learning model using routine clinical EHR data can accurately predict involuntary admission. If implemented as a clinical decision support tool, this model may guide interventions aimed at reducing the risk of involuntary admission.

The aims of this study were to explore the knowledge of EMS physicians (EMSPs) on the legal aspects related to the management of prehospital psychiatric emergencies, assess their degree of comfort, and measure their perceived communication skills.

A survey was distributed to the 376 EMSPs working in the prehospital setting in Lombardy, Italy. Knowledge of medico-legal aspects was measured through multiple-choice questions; the Comfort Score (CS) and perceived Communication Skills Score (CSS) were calculated using Likert Scale questions. CS ranged from 11 points (lowest) to 44 (highest); CSS ranged from 8 points (lowest) to 32 (highest).

A total of 272 EMSPs answered the survey (RR= 72.3%); 45.2% were women. Mean age was 45.76 years (SD 8.8). Mean percentage of correct knowledge test answers was 52.4 (SD 12.3); 81.3% believed to have little knowledge of the Italian legislation regarding this topic. Mean CS was 29.3 (SD 5.5); mean CSS was 19.1 (SD 3.9). A higher percentage of correct answers in the knowledge test was associated with lower CS (P = 0.019). Higher self-appraised knowledge of the Italian legislation was associated with higher CS (P < 0.001) and higher CSS (P < 0.001).

These findings should prompt policy makers to ensure proper training and support for EMSPs in the matter of prehospital psychiatric emergency management.

Tools based on generative artificial intelligence (AI) such as ChatGPT have the potential to transform modern society, including the field of medicine. Due to the prominent role of language in psychiatry, e.g., for diagnostic assessment and psychotherapy, these tools may be particularly useful within this medical field. Therefore, the aim of this study was to systematically review the literature on generative AI applications in psychiatry and mental health.

We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search was conducted across three databases, and the resulting articles were screened independently by two researchers. The content, themes, and findings of the articles were qualitatively assessed.

The search and screening process resulted in the inclusion of 40 studies. The median year of publication was 2023. The themes covered in the articles were mainly mental health and well-being in general – with less emphasis on specific mental disorders (substance use disorder being the most prevalent). The majority of studies were conducted as prompt experiments, with the remaining studies comprising surveys, pilot studies, and case reports. Most studies focused on models that generate language, ChatGPT in particular.

Generative AI in psychiatry and mental health is a nascent but quickly expanding field. The literature mainly focuses on applications of ChatGPT, and finds that generative AI performs well, but notes that it is limited by significant safety and ethical concerns. Future research should strive to enhance transparency of methods, use experimental designs, ensure clinical relevance, and involve users/patients in the design phase.

At the basis of many important research questions is causality – does X causally impact Y? For behavioural and psychiatric traits, answering such questions can be particularly challenging, as they are highly complex and multifactorial. ‘Triangulation’ refers to prospectively choosing, conducting and integrating several methods to investigate a specific causal question. If different methods, with different sources of bias, all indicate a causal effect, the finding is much less likely to be spurious. While triangulation can be a powerful approach, its interpretation differs across (sub)fields and there are no formal guidelines. Here, we aim to provide clarity and guidance around the process of triangulation for behavioural and psychiatric epidemiology, so that results of existing triangulation studies can be better interpreted, and new triangulation studies better designed.

We first introduce the concept of triangulation and how it is applied in epidemiological investigations of behavioural and psychiatric traits. Next, we put forth a systematic step-by-step guide, that can be used to design a triangulation study (accompanied by a worked example). Finally, we provide important general recommendations for future studies.

While the literature contains varying interpretations, triangulation generally refers to an investigation that assesses the robustness of a potential causal finding by explicitly combining different approaches. This may include multiple types of statistical methods, the same method applied in multiple samples, or multiple different measurements of the variable(s) of interest. In behavioural and psychiatric epidemiology, triangulation commonly includes prospective cohort studies, natural experiments and/or genetically informative designs (including the increasingly popular method of Mendelian randomization). The guide that we propose aids the planning and interpreting of triangulation by prompting crucial considerations. Broadly, its steps are as follows: determine your causal question, draw a directed acyclic graph, identify available resources and samples, identify suitable methodological approaches, further specify the causal question for each method, explicate the effects of potential biases and, pre-specify expected results. We illustrated the guide’s use by considering the question: ‘Does maternal tobacco smoking during pregnancy cause offspring depression?’.

In the current era of big data, and with increasing (public) availability of large-scale datasets, triangulation will become increasingly relevant in identifying robust risk factors for adverse mental health outcomes. Our hope is that this review and guide will provide clarity and direction, as well as stimulate more researchers to apply triangulation to causal questions around behavioural and psychiatric traits.

Clinical decision support systems (CDSS) based on machine-learning (ML) models are emerging within psychiatry. If patients do not trust this technology, its implementation may disrupt the patient-clinician relationship. Therefore, the aim was to examine whether receiving basic information about ML-based CDSS increased trust in them.

We conducted an online randomized survey experiment in the Psychiatric Services of the Central Denmark Region. The participating patients were randomized into one of three arms: Intervention = information on clinical decision-making supported by an ML model; Active control = information on a standard clinical decision process, and Blank control = no information. The participants were unaware of the experiment. Subsequently, participants were asked about different aspects of trust and distrust regarding ML-based CDSS. The effect of the intervention was assessed by comparing scores of trust and distrust between the allocation arms.

Out of 5800 invitees, 992 completed the survey experiment. The intervention increased trust in ML-based CDSS when compared to the active control (mean increase in trust: 5% [95% CI: 1%; 9%], p = 0.0096) and the blank control arm (mean increase in trust: 4% [1%; 8%], p = 0.015). Similarly, the intervention reduced distrust in ML-based CDSS when compared to the active control (mean decrease in distrust: −3%[−1%; −5%], p = 0.021) and the blank control arm (mean decrease in distrust: −4% [−1%; −8%], p = 0.022). No statistically significant differences were observed between the active and the blank control arms.

Receiving basic information on ML-based CDSS in hospital psychiatry may increase patient trust in such systems.

Psychiatric research applies statistical methods that can be divided in two frameworks: causal inference and prediction. Recent proposals suggest a down-prioritisation of causal inference and argue that prediction paves the road to ‘precision psychiatry’ (i.e., individualised treatment). In this perspective, we critically appraise these proposals.

We outline strengths and weaknesses of causal inference and prediction frameworks and describe the link between clinical decision-making and counterfactual predictions (i.e., causality). We describe three key causal structures that, if not handled correctly, may cause erroneous interpretations, and three pitfalls in prediction research.

Prediction and causal inference are both needed in psychiatric research and their relative importance is context-dependent. When individualised treatment decisions are needed, causal inference is necessary.

This perspective defends the importance of causal inference for precision psychiatry.

Maternity outcomes for women from certain ethnic groups are notably poor, partly owing to their not receiving treatment from services.

To explore barriers to access among Black and south Asian women with perinatal mental health problems who did not access perinatal mental health services and suggestions for improvements, and to map findings on to the perinatal care pathway.

Semi-structured interviews were conducted in 2020 and 2021 in the UK. Data were analysed using the framework method.

Twenty-three women were interviewed, and various barriers were identified, including limited awareness of services, fear of child removal, stigma and unresponsiveness of perinatal mental health services. Whereas most barriers were related to access, fear of child removal, remote appointments and mask-wearing during COVID-19 affected the whole pathway. Recommendations include service promotion, screening and enhanced cultural understanding.

Women in this study, an underrepresented population in published literature, face societal, cultural, organisational and individual barriers that affect different aspects of the perinatal pathway.