There is an unprecedented societal focus on young people’s mental health, including efforts to expand access to child and adolescent mental health services (CAMHS). There has, however, been a lack of research to date to investigate adult mental health outcomes of young people who attend CAMHS.

We linked Finland’s healthcare registries for all individuals born between 1987 and 1992. We investigated mental disorder diagnoses recorded in specialist adult mental health services (AMHS) and both inpatient and outpatient service use by age 29 (December 31, 2016) for former CAMHS patients.

Before the end of their 20s, more than half (52.4%, n = 21,183) of all CAMHS patients had gone on to attend AMHS. The most prevalent recorded adult psychiatric diagnoses received by former CAMHS patients were depressive disorders (30%, n = 11,768), non-phobic anxiety disorders (21%, n = 7,910), alcohol use disorders (9.5%, n = 3,427), personality disorders (9.3%, n = 3,366), and schizophrenia-spectrum disorders (7.6%, n = 2,945). In the total population, more than half of all AMHS appointments (53.1%, k = 714,239/1,345,060) were for former CAMHS patients. More than half of all inpatient psychiatry bed days were for former CAMHS patients (53.1%, k = 1,192,991/2,245,247).

While there is a strong focus on intervening in childhood and adolescence to reduce the burden of mental illness, these findings suggest that young people who receive childhood intervention very frequently continue to require specialist psychiatric interventions in adulthood, including taking up a majority of both outpatient and inpatient service use. These findings highlight the need for a greater focus on research to alter the long-term trajectories of CAMHS patients.

Relatively little is known about mental healthcare-related harm, with patient safety incidents (PSIs) in community-based services particularly poorly understood. We aimed to characterize PSIs, contributory factors, and reporter-identified solutions within community-based mental health services for working-age adults.

We obtained data on PSIs reported within English services from the National Reporting and Learning System. Of retrieved reports, we sampled all incidents reportedly involving ‘Death’, ‘Severe harm’, or ‘Moderate harm’, and random samples of a proportion of ‘Low harm’ or ‘No harm’ incidents. PSIs and contributory factors were classified through qualitative content analysis using existing frameworks. Frequencies and proportions of incident types were computed, and reporter-identified solutions were inductively categorized.

Of 1825 sampled reports, 1443 were eligible and classified into nine categories. Harmful outcomes, wherein service influence was unclear, were widely observed, with self-harm the modal concern amongst ‘No harm’ (15.0%), ‘Low harm’ (62.8%), and ‘Moderate harm’ (37.6%) categories. Attempted suicides (51.7%) and suicides (52.1%) were the most frequently reported events under ‘Severe harm’ or ‘Death’ outcomes, respectively. Incidents common to most healthcare settings were identified (e.g. medication errors), alongside specialty-specific incidents (e.g. Mental Health Act administration errors). Contributory factors were wide-ranging, with situational failures (e.g. team function failures) and local working conditions (e.g. unmanageable workload) widely reported. Solution categories included service user-directed actions and policy introduction or reinforcement.

Study findings provide novel insights into incidents, contributory factors, and reported solutions within community-based mental healthcare. Targets for safety improvement are outlined, aimed at strengthening system-based prevention of incidents.

Folate and cobalamin deficiency or impaired function due to genetic variants in key enzymes have been associated with neuropsychiatric symptoms. The aim of this study was to compare folate and cobalamin status in patients admitted to an acute psychiatric unit to patients from primary health care in order to reveal factors which may be important in the follow-up of patients with mental disorders.

Anonymous blood samples tested for folate, cobalamin, the metabolic marker total homocysteine (tHcy), creatinine and glomerular filtration rate as well as age and gender in patients admitted to a psychiatric acute unit (n = 981) and patients from primary health care (controls) (n = 32,201) were reviewed retrospectively.

Median serum folate was 18% lower and median serum cobalamin was 11% higher in patients with mental disorders compared to controls. Folate deficiency was associated with 54% higher median tHcy levels among patients with mental disorders compared to controls. The prevalence of folate deficiency was 31% and of cobalamin deficiency 6% in patients admitted to a psychiatric acute unit in a Norwegian hospital in 2024.

Folate, but not cobalamin deficiency, was prevalent in Norwegian patients with mental disorders. The higher tHcy levels in folate-deficient patients with mental disorders indicate an impaired folate metabolism, which might be related to genetic factors, such as polymorphisms in the methylenetetrahydrofolate reductase (MTHFR) gene. Ensuring a serum folate concentration above 15 nmol/L and a serum cobalamin above 250 pmol/L might improve symptoms in patients with mental disorders.

The impact of social determinants of health (SDOH) on mental health is increasingly realized. A comprehensive study examining the associations of SDOH with mental health disorders has yet to be accomplished. This study evaluated the associations between five domains of SDOH and the SDOH summary score and mental health disorders in the United States.

We analyzed data from a diverse group of participants enrolled in the All of Us research programme, a research programme to gather data from one million people living in the United States, in a cross-sectional design. The primary exposure was SDOH based on Healthy People 2030: education access and quality, economic stability, healthcare access and quality, social and community context, and neighbourhood and built environment. A summary SDOH score was calculated by adding each adverse SDOH risk (any SDOH vs. no SDOH). Our primary outcomes were diagnoses of major depression (MD) (i.e., major depressive disorder, recurrent MD or MD in remission) and anxiety disorders (AD) (i.e., generalized AD and other anxiety-related disorders). Multiple logistic regression models were used to determine adjusted odd ratios (aORs) for MD and/or ADs after controlling for covariates.

A total of 63,162 participants with MD were identified (22,277 [35.3%] age 50–64 years old; 41,876 [66.3%] female). A total of 77,624 participants with AD were identified (25,268 [32.6%] age 50–64 years old; 52,224 [67.3%] female). Factors associated with greater odds of MD and AD included having less than a college degree, annual household income less than 200% of federal poverty level, housing concerns, lack of transportation, food insecurity, and unsafe neighbourhoods. Having no health insurance was associated with lower odds of both MD and AD (aOR, 0.48; 95% confidence interval [CI], 0.46–0.51 and aOR, 0.44; 95% CI, 0.42–0.47, respectively). SDOH summary score was strongly associated with the likelihood of having MD and AD (aOR, 1.97; 95% CI, 1.89–2.06 and aOR, 1.69; 95% CI, 1.63–1.75, respectively).

This study found associations between all five domains of SDOH and the higher odds of having MD and/or AD. The strong correlations between the SDOH summary score and mental health disorders indicate a possible use of the summary score as a measure of risk of developing mental health disorders.

Adolescence is a critical developmental phase during which young people are vulnerable to the experiences of mental ill-health and social exclusion (consisting of various domains including education and employment, housing, finances and social supports and relationships). The aims of this study were to (i) obtain an understanding of the relationships between social exclusion, mental health and wellbeing of young people; and (ii) identify potentially modifiable targets, or population groups that require greater or targeted supports.

Data were obtained from the Mission Australia 2022 Youth Survey, Australia’s largest annual population-wide survey of young people aged 15–19 years (n = 18,800). Participants’ experiences of social exclusion in different domains were explored (e.g., prevalence, co-occurrence and controlling for differences in demographic characteristics). Multivariable linear regression models were used to map the relationships between social exclusion domains and mental health and wellbeing, controlling for confounding factors where necessary.

Sixty per cent of all young people experienced social exclusion in at least one domain, 25% in multiple. Young people who identified as gender diverse, Indigenous, living in a remote/rural or socio-economically disadvantaged area and with a culturally diverse background were more likely to report social exclusion. A strong association was seen between all domains of social exclusion and poor mental health (e.g., higher psychological distress and loneliness, reduced personal wellbeing, reduced sense of control over their life and a more negative outlook on the future). Notably, difficulties in socialising and obtaining social support were critical factors linked to increased psychological distress and reduced wellbeing.

Findings underscore the need to address multiple domains of social exclusion concurrently, and in collaboration with youth mental healthcare. Prevention efforts aimed at early identification and intervention should be prioritised to support young people vulnerable to social exclusion. Screening approaches are needed to identify individuals and groups of young people in need of support, and to facilitate care coordination across multiple providers.

Cardiovascular disease (CVD) is twice as prevalent among individuals with mental illness compared to the general population. Prevention strategies exist but require accurate risk prediction. This study aimed to develop and validate a machine learning model for predicting incident CVD among patients with mental illness using routine clinical data from electronic health records.

A cohort study was conducted using data from 74,880 patients with 1.6 million psychiatric service contacts in the Central Denmark Region from 2013 to 2021. Two machine learning models (XGBoost and regularised logistic regression) were trained on 85% of the data from six hospitals using 234 potential predictors. The best-performing model was externally validated on the remaining 15% of patients from another three hospitals. CVD was defined as myocardial infarction, stroke, or peripheral arterial disease.

The best-performing model (hyperparameter-tuned XGBoost) demonstrated acceptable discrimination, with an area under the receiver operating characteristic curve of 0.84 on the training set and 0.74 on the validation set. It identified high-risk individuals 2.5 years before CVD events. For the psychiatric service contacts in the top 5% of predicted risk, the positive predictive value was 5%, and the negative predictive value was 99%. The model issued at least one positive prediction for 39% of patients who developed CVD.

A machine learning model can accurately predict CVD risk among patients with mental illness using routinely collected electronic health record data. A decision support system building on this approach may aid primary CVD prevention in this high-risk population.

Cognitive therapy for PTSD (CT-PTSD) is an efficacious treatment for children and adolescents with post-traumatic stress disorder (PTSD) following single incident trauma, but there is a lack of evidence relating to this approach for youth with PTSD following exposure to multiple traumatic experiences.

To assess the safety, acceptability and feasibility of CT-PTSD for youth following multiple trauma, and obtain a preliminary estimate of its pre–post effect size.

Nine children and adolescents (aged 8–17 years) with multiple-trauma PTSD were recruited to a case series of CT-PTSD. Participants completed a structured interview and mental health questionnaires at baseline, post-treatment and 6-month follow-up, and measures of treatment credibility, therapeutic alliance, and mechanisms proposed to underpin treatment response. A developmentally adjusted algorithm for diagnosing PTSD was used.

No safety concerns or adverse effects were recorded. Suicidal ideation reduced following treatment. No participants withdrew from treatment or from the study. CT-PTSD was rated as highly credible. Participants reported strong working alliances with their therapists. Data completion was good at post-treatment (n=8), but modest at 6-month follow-up (n=6). Only two participants met criteria for PTSD (developmentally adjusted algorithm) at post-treatment. A large within-subjects treatment effect was observed post-treatment and at follow up for PTSD severity (using self-report questionnaire measures; ds>1.65) and general functioning (CGAS; ds<1.23). Participants showed reduced anxiety and depression symptoms at post-treatment and follow-up (RCADS-C; ds>.57).

These findings suggest that CT-PTSD is a safe, acceptable and feasible treatment for children with multiple-trauma PTSD, which warrants further evaluation.