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.

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.

Late-life depression has been associated with volume changes of the hippocampus. However, little is known about its association with specific hippocampal subfields over time.

We investigated whether hippocampal subfield volumes were associated with prevalence, course and incidence of depressive symptoms.

We extracted 12 hippocampal subfield volumes per hemisphere with FreeSurfer v6.0 using T1-weighted and fluid-attenuated inversion recovery 3T magnetic resonance images. Depressive symptoms were assessed at baseline and annually over 7 years of follow-up (9-item Patient Health Questionnaire). We used negative binominal, logistic, and Cox regression analyses, corrected for multiple comparisons, and adjusted for demographic, cardiovascular and lifestyle factors.

A total of n = 4174 participants were included (mean age 60.0 years, s.d. = 8.6, 51.8% female). Larger right hippocampal fissure volume was associated with prevalent depressive symptoms (odds ratio (OR) = 1.26, 95% CI 1.08–1.48). Larger bilateral hippocampal fissure (OR = 1.37–1.40, 95% CI 1.14–1.71), larger right molecular layer (OR = 1.51, 95% CI 1.14–2.00) and smaller right cornu ammonis (CA)3 volumes (OR = 0.61, 95% CI 0.48–0.79) were associated with prevalent depressive symptoms with a chronic course. No associations of hippocampal subfield volumes with incident depressive symptoms were found. Yet, lower left hippocampal amygdala transition area (HATA) volume was associated with incident depressive symptoms with chronic course (hazard ratio = 0.70, 95% CI 0.55–0.89).

Differences in hippocampal fissure, molecular layer and CA volumes might co-occur or follow the onset of depressive symptoms, in particular with a chronic course. Smaller HATA was associated with an increased risk of incident (chronic) depression. Our results could capture a biological foundation for the development of chronic depressive symptoms, and stresses the need to discriminate subtypes of depression to unravel its biological underpinnings.