Most studies on the impact of the COVID-19 pandemic on depression burden focused on the earlier pandemic phase specific to lockdowns, but the longer-term impact of the pandemic is less well studied. In this population-based cohort study with quasi-experimental design, we examined both the short-term and long-term impacts of COVID-19 on depression incidence and healthcare service use among patients with depression.

Using the territory-wide electronic medical records in Hong Kong, we identified patients with new diagnoses of depression from 2014 to 2022. An interrupted time-series (ITS) analysis examined changes in incidence of depression before and during the pandemic. We then divided patients into nine cohorts based on year of incidence and studied their initial and ongoing service use until December 2022. Generalized linear modeling compared the rates of healthcare service use in the year of diagnosis between patients newly diagnosed before and during the pandemic. A separate ITS analysis explored the pandemic impact on the ongoing service use among preexisting patients.

There was an immediate increase in depression incidence (RR=1.21; 95% CI: 1.10, 1.33; p<0.001) in the population since the pandemic with a nonsignificant slope change, suggesting a sustained effect until the end of 2022. Subgroup analysis showed that increases in incidence were significant among adults and the older population, but not adolescents. Depression patients newly diagnosed during the pandemic used 11 percent fewer resources than the prepandemic patients in the first diagnosis year. Preexisting depression patients also had an immediate decrease of 16 percent in overall all-cause service use since the pandemic, with a positive slope change indicating a gradual rebound.

During the COVID-19 pandemic, service provision for depression was suboptimal in the face of greater demand generated by the increasing depression incidence. Our findings indicate the need to improve mental health resource planning preparedness for future public health crises.

We developed a real-world evidence (RWE) based Markov model to project the 10-year cost of care for patients with depression from the public payer’s perspective to inform early policy and resource planning in Hong Kong.

The model considered treatment-resistant depression (TRD) and development of comorbidities along the disease course. The outcomes included costs for all-cause and psychiatric care. From our territory-wide electronic medical records, we identified 25,190 patients with newly diagnosed depression during the period from 2014 to 2016, with follow-up until December 2020 for real-world time-to-event patterns. Costs and time varying transition inputs were derived using negative binomial and parametric survival modeling. The model is available as a closed cohort, which studies a fixed cohort of incident patients, or an open cohort that introduces new patients every year. Utilities values and the number of incident cases per year were derived from published sources.

There were 9,217 new patients with depression in 2023. Our closed cohort model projected that the cumulative cost of all-cause and psychiatric care for these patients would reach USD309 million and USD58 million by 2032, respectively. In our open cohort model, 55,849 to 57,896 active prevalent cases would cost more than USD322 million and USD61 million annually in all-cause and psychiatric care, respectively. Although less than 20 percent of patients would develop TRD or its associated comorbidities, they contribute 31 to 54 percent of the costs. The key cost drivers were the number of annual incident cases and the probability of developing TRD and associated comorbidities and of becoming a low-intensity service user. These factors are relevant to early disease stages.

A small proportion of patients with depression develop TRD, but they contribute to a high proportion of the care costs. Our projection also demonstrates the application of RWE to model the long-term costs of care, which can aid policymakers in anticipating foreseeable burden and undertaking budget planning to prepare for future care needs.

Population-wide restrictions during the COVID-19 pandemic may create barriers to mental health diagnosis. This study aims to examine changes in the number of incident cases and the incidence rates of mental health diagnoses during the COVID-19 pandemic.

By using electronic health records from France, Germany, Italy, South Korea and the UK and claims data from the US, this study conducted interrupted time-series analyses to compare the monthly incident cases and the incidence of depressive disorders, anxiety disorders, alcohol misuse or dependence, substance misuse or dependence, bipolar disorders, personality disorders and psychoses diagnoses before (January 2017 to February 2020) and after (April 2020 to the latest available date of each database [up to November 2021]) the introduction of COVID-related restrictions.

A total of 629,712,954 individuals were enrolled across nine databases. Following the introduction of restrictions, an immediate decline was observed in the number of incident cases of all mental health diagnoses in the US (rate ratios (RRs) ranged from 0.005 to 0.677) and in the incidence of all conditions in France, Germany, Italy and the US (RRs ranged from 0.002 to 0.422). In the UK, significant reductions were only observed in common mental illnesses. The number of incident cases and the incidence began to return to or exceed pre-pandemic levels in most countries from mid-2020 through 2021.

Healthcare providers should be prepared to deliver service adaptations to mitigate burdens directly or indirectly caused by delays in the diagnosis and treatment of mental health conditions.

Schizophrenia is a complex and heterogeneous syndrome with high clinical and biological stratification. Identifying distinctive subtypes can improve diagnostic accuracy and help precise therapy. A key challenge for schizophrenia subtyping is understanding the subtype-specific biological underpinnings of clinical heterogeneity. This study aimed to investigate if the machine learning (ML)-based neuroanatomical and symptomatic subtypes of schizophrenia are associated.

A total of 314 schizophrenia patients and 257 healthy controls from four sites were recruited. Gray matter volume (GMV) and Positive and Negative Syndrome Scale (PANSS) scores were employed to recognize schizophrenia neuroanatomical and symptomatic subtypes using K-means and hierarchical methods, respectively.

Patients with ML-based neuroanatomical subtype-1 had focally increased GMV, and subtype-2 had widespread reduced GMV than the healthy controls based on either K-means or Hierarchical methods. In contrast, patients with symptomatic subtype-1 had severe PANSS scores than subtype-2. No differences in PANSS scores were shown between the two neuroanatomical subtypes; similarly, no GMV differences were found between the two symptomatic subtypes. Cohen’s Kappa test further demonstrated an apparent dissociation between the ML-based neuroanatomical and symptomatic subtypes (P > 0.05). The dissociation patterns were validated in four independent sites with diverse disease progressions (chronic vs. first episodes) and ancestors (Chinese vs. Western).

These findings revealed a replicable dissociation between ML-based neuroanatomical and symptomatic subtypes of schizophrenia, which provides a new viewpoint toward understanding the heterogeneity of schizophrenia.

To assess the prevalence, severity and socio-demographic predictors of household food insecurity among vulnerable women accessing the Canada Prenatal Nutrition Program (CPNP) and to examine associations between household food insecurity and breastfeeding practices to 6 months.

Cohort investigation pooling data from two studies which administered the 18-item Household Food Security Survey Module at 6 months postpartum and collected prospective infant feeding data at 2 weeks and 2, 4 and 6 months. Household food insecurity was classified as none, marginal, moderate or severe. Logistic regression analyses were performed to assess predictors of household food insecurity and associations between household food security (any and severity) and continued and exclusive breastfeeding.

Three Toronto sites of the CPNP, a federal initiative targeting socially and/or economically vulnerable women.

316 birth mothers registered prenatally in the CPNP from 2017 to 2020.

Household food insecurity at 6 months postpartum was highly prevalent (44 %), including 11 % in the severe category. Risk of household food insecurity varied by CPNP site (P < 0·001) and was higher among multiparous participants (OR 2·08; 95 % CI 1·28, 3·39). There was no association between the prevalence or severity of food insecurity and continued or exclusive breastfeeding to 6 months postpartum in the adjusted analyses.

Household food insecurity affected nearly half of this cohort of women accessing the CPNP. Further research is needed on household food insecurity across the national CPNP and other similar programmes, with consideration of the implications for programme design, service delivery and policy responses.

There is increasing research examining excess mortality in people with bipolar disorder using life expectancy and related measures, which quantify the disease impact on survival. However, there has been no meta-analysis to date summarising existing data on life expectancy in those with bipolar disorder.

To systematically review and quantitatively synthesise estimates of life expectancy and years of potential life lost (YPLL) in people with bipolar disorder.

We searched Embase, Medline, PsycINFO and Web of Science databases up to 31 March 2021. We generated pooled life expectancy using random-effects models, and derived YPLL summary estimate by calculating averaged values weighted by sample size of individual studies. Subgroup analyses were conducted for gender, geographical region, study period, a given age (set-age) for lifespan estimation and causes of death. The study was registered with PROSPERO (CRD42021241705).

Eleven and 13 studies were included in the review for life expectancy (n = 96 601) and YPLL (n = 128 989), respectively. Pooled life expectancy was 66.88 years (95% CI 64.47–69.28; I2 = 99.9%, P < 0.001), was higher in women than men (70.51 (95% CI 68.61–72.41) v. 64.59 (95% CI 61.16–68.03); z = 2.00, P = 0.003) and was lowest in Africa. Weighted average YPLL was 12.89 years (95% CI 12.72–13.07), and was greatest in Africa. More YPLL was observed when lifespan was estimated at birth than at other set-age. YPLLs attributable to natural and unnatural deaths were 5.94 years (95% CI 5.81–6.07) and 5.69 years (95% CI 5.59–5.79), respectively.

Bipolar disorder is associated with substantially shortened life expectancy. Implementation of multilevel, targeted interventions is urgently needed to reduce this mortality gap.

Contrasting the well-described effects of early intervention (EI) services for youth-onset psychosis, the potential benefits of the intervention for adult-onset psychosis are uncertain. This paper aims to examine the effectiveness of EI on functioning and symptomatic improvement in adult-onset psychosis, and the optimal duration of the intervention.

360 psychosis patients aged 26–55 years were randomized to receive either standard care (SC, n = 120), or case management for two (2-year EI, n = 120) or 4 years (4-year EI, n = 120) in a 4-year rater-masked, parallel-group, superiority, randomized controlled trial of treatment effectiveness (Clinicaltrials.gov: NCT00919620). Primary (i.e. social and occupational functioning) and secondary outcomes (i.e. positive and negative symptoms, and quality of life) were assessed at baseline, 6-month, and yearly for 4 years.

Compared with SC, patients with 4-year EI had better Role Functioning Scale (RFS) immediate [interaction estimate = 0.008, 95% confidence interval (CI) = 0.001–0.014, p = 0.02] and extended social network (interaction estimate = 0.011, 95% CI = 0.004–0.018, p = 0.003) scores. Specifically, these improvements were observed in the first 2 years. Compared with the 2-year EI group, the 4-year EI group had better RFS total (p = 0.01), immediate (p = 0.01), and extended social network (p = 0.05) scores at the fourth year. Meanwhile, the 4-year (p = 0.02) and 2-year EI (p = 0.004) group had less severe symptoms than the SC group at the first year.

Specialized EI treatment for psychosis patients aged 26–55 should be provided for at least the initial 2 years of illness. Further treatment up to 4 years confers little benefits in this age range over the course of the study.

Schizophrenia is a longstanding condition and most patients experience multiple relapse in the course of the condition. High expressed emotion (HEE) has been found to be a predictor of relapse. This meta-analysis and meta-regression examined the association of global EE and relapse specifically focusing on timing of relapse and EE domains.

Random-effects model was used to pool the effect estimates. Multiple random-effects meta-regression was used to compute the moderator analysis. Putative effect moderators including culture, EE measurements, age, length of condition and study quality were included.

Thirty-three prospective cohort studies comprising 2284 patients were included in the descriptive review and 30 studies were included for meta-analysis and meta-regression. Findings revealed that global HEE significantly predicted more on early relapse (⩽12 months) [OR 4.87 (95% CI 3.22–7.36)] than that on late relapse (>12 months) [OR 2.13 (95% CI 1.36–3.35)]. Higher level of critical comments (CC) significantly predicted relapse [OR 2.22 (95% CI 1.16–4.26)], whereas higher level of warmth significantly protected patients from relapse [OR 0.35 (95% CI 0.15–0.85)]. None of the moderators included significantly change the results.

These findings indicate that there is a dynamic interaction between EE-relapse association with time, and CC and warmth are the two important EE domains to influence relapse among patients with schizophrenia. Results also confirmed the foci of family interventions on reducing CC and improving warmth in relationship.