Data on trends in the epidemiological burden of bipolar disorder are scarce.

To provide an overview of trends in bipolar disorder burden from 1990 to 2019.

Revisiting the Global Burden of Disease Study 2019, we analysed the number of cases, calculated the age-standardised rate (per 100 000 population) and estimated annual percentage change (EAPC) of incidence, prevalence and years lived with disability (YLDs) for bipolar disorder from 1990 to 2019. The independent effects of age, period and cohort were estimated by the age–period–cohort modelling.

Globally, the bipolar disorder-related prevalent cases, incident cases and number of YLDs all increased from 1990 to 2019. Regionally, the World Health Organization Region of the Americas accounted for the highest estimated YLD number and rate, with the highest age-standardised prevalence rate in 1990 and 2019 and highest EAPC of prevalence. By sociodemographic index (SDI) quintiles, all five SDI regions saw an increase in estimated incident cases. Nationally, New Zealand reported the highest age-standardised rate of incidence, prevalence and YLDs in 1990 and 2019. The most prominent age effect on incidence rate was in those aged 15–19 years. Decreased effects of period on incidence, prevalence and YLD rates was observed overall and in females, not in males. The incidence, prevalence and YLD rates showed an unfavourable trend in the younger cohorts born after 1990, with males reporting a higher cohort risk than females.

From 1990 to 2019, the overall trend of bipolar disorder burden presents regional and national variations and differs by age, sex, period and cohort.

Enduring ethnic inequalities exist in mental healthcare. The COVID-19 pandemic has widened these.

To explore stakeholder perspectives on how the COVID-19 pandemic has increased ethnic inequalities in mental healthcare.

A qualitative interview study of four areas in England with 34 patients, 15 carers and 39 mental health professionals from National Health Service (NHS) and community organisations (July 2021 to July 2022). Framework analysis was used to develop a logic model of inter-relationships between pre-pandemic barriers and COVID-19 impacts.

Impacts were largely similar across sites, with some small variations (e.g. positive service impacts of higher ethnic diversity in area 2). Pre-pandemic barriers at individual level included mistrust and thus avoidance of services and at a service level included the dominance of a monocultural model, leading to poor communication, disengagement and alienation. During the pandemic remote service delivery, closure of community organisations and media scapegoating exacerbated existing barriers by worsening alienation and communication barriers, fuelling prejudice and division, and increasing mistrust in services. Some minority ethnic patients reported positive developments, experiencing empowerment through self-determination and creative activities.

During the COVID-19 pandemic some patients showed resilience and developed adaptations that could be nurtured by services. However, there has been a reduction in the availability of group-specific NHS and third-sector services in the community, exacerbating pre-existing barriers. As these developments are likely to have long-term consequences for minority ethnic groups’ engagement with mental healthcare, they need to be addressed as a priority by the NHS and its partners.

Adverse childhood experiences (ACEs) are well-known risk factors for schizophrenia and bipolar disorder.

The aim was to study the associations between specific ACEs and psychological functioning in women with schizophrenia or bipolar disorder.

Among 29 367 women (mean age 44 years) from the Icelandic Stress-And-Gene-Analysis (SAGA) study, 534 (1.8%, mean age 40) reported having been diagnosed with schizophrenia or bipolar disorder, which were combined to ‘severe mental disorders’. Participants reported on 13 types of ACEs, childhood deprivation and psychological functioning (defined as coping ability and current symptoms of depression, anxiety and sleep disturbances). Adjusted Poisson regression calculated prevalence ratios (PRs) between ACEs and severe mental disorders. Linear regression assessed the association between ACEs and psychological functioning among women with a severe mental disorder.

Women with a severe mental disorder reported more ACEs (mean 4.57, s.d. = 2.82) than women without (mean 2.51, s.d. = 2.34) in a dose-dependent manner (fully-adjusted PR = 1.23 per ACE, 95% CI 1.20–1.27). After mutual adjustment for other ACEs, emotional abuse, sexual abuse, mental illness of a household member, emotional neglect, bullying and collective violence were associated with severe mental disorders. Among women with severe mental disorders, a higher number of ACEs was associated with increased symptom burden of depression (β = 2.79, 95% CI = 1.19–4.38) and anxiety (β = 2.04, 95% CI = 0.99–3.09) including poorer sleep quality (β = 0.83, 95% CI = 0.07–1.59). Findings were similar for schizophrenia and bipolar disorder separately.

Women with schizophrenia or bipolar disorder show a strong history of ACEs, which may interfere with their psychological functioning and, therefore, need to be addressed as part of their treatment, for example, with trauma-focused psychotherapy.

Forensic patients with psychosis often engage in violent behaviour. There has been significant progress in understanding risk factors for violence, but identification of causal mechanisms of violence is limited.

To develop a testable psychological framework explaining violence in psychosis – grounded in patient experience – to guide targeted treatment development.

We conducted in-depth interviews with 20 patients with psychosis using forensic psychiatric services across three regions in England. Interviews were analysed using reflexive thematic analysis. People with lived experience contributed to the analysis.

Analysis of interviews identified several psychological processes involved in the occurrence of violence. Violence was the dominant response mode to difficulties that was both habitual and underpinned by rules that engaged and justified an attack. Violence was triggered by a trio of sensitivities to other people: sensitivity to physical threat, from which violence protected; sensitivity to social disrespect, by which violence increased status; and sensitivity to unfairness, by which violence delivered revenge. Violence was an attempt to regulate difficult internal states: intense emotions were released through aggression and violence was an attempt to escape being overwhelmed by voices, visions or paranoia. There were different patterns of emphasis across these processes when explaining an individual participant's offending behaviour.

The seven-factor model of violence derived from our analysis of patient accounts highlights multiple modifiable psychological processes that can plausibly lead to violence. The model can guide the research and development of targeted treatments to reduce violence by individuals with psychosis.

Computational models offer promising potential for personalised treatment of psychiatric diseases. For their clinical deployment, fairness must be evaluated alongside accuracy. Fairness requires predictive models to not unfairly disadvantage specific demographic groups. Failure to assess model fairness prior to use risks perpetuating healthcare inequalities. Despite its importance, empirical investigation of fairness in predictive models for psychiatry remains scarce.

To evaluate fairness in prediction models for development of psychosis and functional outcome.

Using data from the PRONIA study, we examined fairness in 13 published models for prediction of transition to psychosis (n = 11) and functional outcome (n = 2) in people at clinical high risk for psychosis or with recent-onset depression. Using accuracy equality, predictive parity, false-positive error rate balance and false-negative error rate balance, we evaluated relevant fairness aspects for the demographic attributes ‘gender’ and ‘educational attainment’ and compared them with the fairness of clinicians’ judgements.

Our findings indicate systematic bias towards assigning less favourable outcomes to individuals with lower educational attainment in both prediction models and clinicians’ judgements, resulting in higher false-positive rates in 7 of 11 models for transition to psychosis. Interestingly, the bias patterns observed in algorithmic predictions were not significantly more pronounced than those in clinicians’ predictions.

Educational bias was present in algorithmic and clinicians’ predictions, assuming more favourable outcomes for individuals with higher educational level (years of education). This bias might lead to increased stigma and psychosocial burden in patients with lower educational attainment and suboptimal psychosis prevention in those with higher educational attainment.

The psychological therapies service (PTS) in the Northern Health and Social Care Trust, in Northern Ireland, provides therapies to adults with moderate or severe mental health difficulties. Psychometric outcomes data are routinely collected to assess if a patient demonstrates significant improvement in their main presenting problem area following therapy. The wider impact of therapy is not fully measured in the outcomes database as this would be disproportionately burdensome for both patient and therapist. The present study, to our knowledge, is the first to use data linkage to link patient therapy outcomes data with prescriptions data.

To widen our understanding of patient medication use before and after therapy.

Using Health and Care Number as a unique identifier, the Psychological Therapies Service – Routine Outcome Measurement Database (n = 3625) and data from 72 500 controls were linked with data from the Enhanced Prescribing Database (EPD). The EPD data were sourced from the Honest Broker Service.

Key findings from the study were: (a) the odds of PTS clients using antipsychotics in the year before therapy were 25 times greater compared with controls (odds ratio (OR) = 24.53, 95% CI 20.16–29.84); (b) in the 1st year post discharge, PTS clients who clinically improved post therapy discharge were more likely than ‘non-engagers’ and ‘non-improvers’ to come off antianxiety medication (OR = 0.61, 95%, CI 0.38–0.98); and (c) therapy did not have an impact on antidepressant use.

The results highlight the need for discussion between therapy services, GPs and psychiatry about whether more engagement and collaboration is needed to plan phased reduction in medication.

Despite its significance, ensuring continuity of care demands substantial resources, which might not be readily accessible in many public healthcare systems. Studies indicate that continuity of care remains uncertain in numerous healthcare systems.

This study aimed to assess the effectiveness of a continuity-of-care model for patients with severe mental illness (SMI), providing seamless treatment from discharge from a closed ward to subsequent psychiatric, psychological and rehabilitation services.

Data from patients discharged before (1 January to 31 December 2018) and after (1 June 2021 to 31 May 2022) full implementation of the model were analysed and compared in terms of average duration of hospital stay, emergency department visits within 90 days of discharge, readmission rate within a year post-discharge and initiation of rehabilitation process.

In the post-implementation period (n = 482), the average admission time significantly decreased from 30.51 ± 29.72 to 26.77 ± 27.89 days, compared with the pre-implementation period (n = 403) (P = 0.029). Emergency department visits within 90 days following discharge decreased from 38.70 to 26.35% of discharged patients (P < 0.001). The rate of readmission decreased from 50.9 to 44.0% (P = 0.041) for one readmission and from 28.3 to 22.0% (P = 0.032) for two readmissions in the year following discharge. Additionally, the proportion of patients entering formal rehabilitation increased from 7.94 to 12.03% (P = 0.044).

This study highlights the effectiveness of a continuity-of-care model spearheaded by senior psychiatrists and involving paramedical personnel. These findings underscore the significant potential of the model to substantially enhance mental health services and outcomes. Moreover, they emphasise its relevance for patients, clinicians and policy makers.

Profiling patients on a proposed ‘immunometabolic depression’ (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment.

To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants.

Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses.

Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, βpooled = 0.06, P = 0.049, 95% CI 0.0001–0.12, I2 = 3.61%); this was also found for an IMD index combining these features (n = 372, βpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01–0.22, I2 = 23.91%), with a higher – but still small – effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (βpooled = 0.16) and the IMD index (βpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission.

Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.

International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics.

To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity.

Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results.

Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0–82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%.

The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.