Childhood trauma is a well-established risk factor for psychosis, paranoia, and substance use, with cannabis being a modifiable environmental factor that exacerbates these vulnerabilities. This study examines the interplay between childhood trauma, cannabis use, and paranoia using standard tetrahydrocannabinol (THC) units as a comprehensive measure of cannabis exposure.

Data were derived from the Cannabis&Me study, an observational, cross-sectional, online survey of 4,736 participants. Childhood trauma was assessed using a modified Childhood Trauma Screen Questionnaire, while paranoia was measured via the Green Paranoid Thoughts Scale. Cannabis use was quantified using weekly standard THC units. Structural equation modeling (SEM) was employed to evaluate direct and indirect pathways between trauma, cannabis use, and paranoia.

Childhood trauma was strongly associated with paranoia, particularly emotional, and physical abuse (β = 16.10, q < 0.001; β = 16.40, q < 0.001). Cannabis use significantly predicted paranoia (β = 0.009, q < 0.001). Interactions emerged between standard THC units and both emotional abuse (β = 0.011, q < 0.001) and household discord (β = 0.011, q < 0.001). SEM revealed a small but significant indirect effect of trauma on paranoia via cannabis use (β = 0.004, p = 0.017).

These findings highlight childhood trauma as a primary driver of paranoia, with cannabis use amplifying its effects. While trauma had a strong direct impact, cannabis played a significant mediating role. Integrating standard THC units into psychiatric research and clinical assessments may enhance risk detection and refine intervention strategies, particularly for childhood trauma-exposed individuals.

Diagnostic accuracy is an unmet need for major depressive disorder (MDD) and major depressive episode (MDE) in bipolar disorder. Very limited research has evaluated bipolar disorder/MDE and MDD using ecological momentary assessment (EMA) time-series data.

We aimed to examine differentiating phenomenological characteristics in positive affect dynamics, and temporal relationships with pleasure towards current activity and meaning in life (MIL), among MDD, MDE/bipolar disorder and healthy controls using EMA.

Participants (N = 88, mean age 28.7 years, 69% female), including individuals with MDD (n = 29) and MDE/bipolar disorder (n = 29) and healthy controls (n = 30), were assessed for positive affect, pleasure and MIL 5 times daily over a 2-week period. Multilevel modelling analysis was conducted, with estimation of first-order autoregressive model structure and time-lagged relationship between pleasure and positive affect.

From 4632 EMA observations, positive affect dynamics (inertia, variability and instability) did not differ significantly across groups (all P > 0.05). Although all groups demonstrated a bidirectional relationship between positive affect and pleasure, for MDE/bipolar disorder, both pleasuret − 1 (β = −0.11, t[51.09] = −2.31, P = 0.025) and positive affectt − 1 (β = −0.13, t[56.54] = −2.30, P = 0.025) predicted subsequent MIL less significantly than for MDD and healthy controls.

Individuals with MDE/bipolar disorder, but not MDD, had less self-reported MIL from positive affect and pleasure. There is little evidence that emotional experience alone characterises the pathophysiology between MDD and MDE/bipolar disorder; such investigation may be limited by within-group heterogeneity. Our findings provide a new perspective on using a time-series approach beyond bimodal measures in EMA to differentiate bipolar disorder/MDE and MDD.

Second-generation antipsychotics (SGAs) cause metabolic side effects. However, patients’ metabolic profiles were influenced by time-invariant and time-varying confounders. Real-world evidence on the long-term, dynamic effects of SGAs (e.g. different treatment sequences) are limited. We employed advanced causal inference methods to evaluate the metabolic impact of SGAs in a naturalistic cohort.

We followed 696 Chinese patients with schizophrenia-spectrum disorders receiving SGAs. Longitudinal targeted maximum likelihood estimation (LTMLE) was used to estimate the average treatment effects (ATEs) of continuous SGA treatment versus ‘no treatment’ on metabolic outcomes, including total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglyceride (TG), fasting glucose (FG), and body mass index (BMI), over 6–18 months at 3-month intervals. LTMLE accounted for time-invariant and time-varying confounders. Post-SGA discontinuation side effects were also assessed.

The ATEs of continuous SGA treatment on BMI and TG showed an inverted U-shaped pattern, peaking at 12 months and declining afterwards. Similar patterns were observed for TC and LDL, albeit the ATEs peaked at 15 months. For FG and HDL, the ATEs peaked at ~6 months. The adverse impact of SGAs on BMI persisted even after medication discontinuation, yet other metabolic parameters did not show such lingering side effects. Clozapine and olanzapine exhibited greater metabolic side effects compared to other SGAs.

Our real-world study suggests that metabolic side effects may stabilize with prolonged continuous treatment. Clozapine and olanzapine confer higher cardiometabolic risks than other SGAs. The side effects of SGAs on BMI may persist after drug discontinuation. These insights may guide antipsychotic choice and improve management of metabolic side effects.

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.

Accumulating studies have assessed mortality risk associated with mood-stabilizers, the mainstay treatment for bipolar disorder (BD). However, existing data were mostly restricted to suicide risk, focused on lithium and valproate and rarely adequately adjusted for potential confounders. This study aimed to assess comparative mortality risk with all, natural and unnatural causes between lithium, valproate and three frequently prescribed second-generation antipsychotics (SGA), with adjustment for important confounders.

This population-based cohort study identified 8137 patients with first-diagnosed BD, who had exposed to lithium (n = 1028), valproate (n = 3580), olanzapine (n = 797), quetiapine (n = 1975) or risperidone (n = 757) between 2002 and 2018. Data were retrieved from territory-wide medical-record database of public healthcare services in Hong Kong. Propensity-score (PS)-weighting method was applied to optimize control for potential confounders including pre-existing chronic physical diseases, substance/alcohol use disorders and other psychotropic medications. PS-weighted Cox proportional-hazards regression was conducted to assess risk of all-, natural- and unnatural-cause mortality related to each mood-stabilizer, compared to lithium. Three sets of sensitivity analyses were conducted by restricting to patients with (i) length of cumulative exposure to specified mood-stabilizer ≥90 days and its medication possession ratio (MPR) ≥90%, (ii) MPR of specified mood-stabilizer ≥80% and MPR of other studied mood-stabilizers <20% and (iii) monotherapy.

Incidence rates of all-cause mortality per 1000 person-years were 5.9 (95% confidence interval [CI]: 4.5–7.6), 8.4 (7.4–9.5), 11.1 (8.3–14.9), 7.4 (6.0–9.2) and 12.0 (9.3–15.6) for lithium-, valproate-, olanzapine-, quetiapine- and risperidone-treated groups, respectively. BD patients treated with olanzapine (PS-weighted hazard ratio = 2.07 [95% CI: 1.33–3.22]) and risperidone (1.66 [1.08–2.55]) had significantly higher all-cause mortality rate than lithium-treated group. Olanzapine was associated with increased risk of natural-cause mortality (3.04 [1.54–6.00]) and risperidone was related to elevated risk of unnatural-cause mortality (3.33 [1.62–6.86]), relative to lithium. The association between olanzapine and increased natural-cause mortality rate was consistently affirmed in sensitivity analyses. Relationship between risperidone and elevated unnatural-cause mortality became non-significant in sensitivity analyses restricted to low MPR in other mood-stabilizers and monotherapy. Valproate- and lithium-treated groups did not show significant differences in all-, natural- or unnatural-cause mortality risk.

Our data showed that olanzapine and risperidone were associated with higher mortality risk than lithium, and further supported the clinical guidelines recommending lithium as the first-line mood-stabilizer for BD. Future research is required to further clarify comparative mortality risk associated with individual SGA agents to facilitate risk-benefit evaluation of alternative mood-stabilizers to minimize avoidable premature mortality in BD.

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.

Patients with bipolar disorder (BPD) are prone to engage in risk-taking behaviours and self-harm, contributing to higher risk of traumatic injuries requiring medical attention at the emergency room (ER).We hypothesize that pharmacological treatment of BPD could reduce the risk of traumatic injuries by alleviating symptoms but evidence remains unclear. This study aimed to examine the association between pharmacological treatment and the risk of ER admissions due to traumatic injuries.

Individuals with BPD who received mood stabilizers and/or antipsychotics were identified using a population-based electronic healthcare records database in Hong Kong (2001–2019). A self-controlled case series design was applied to control for time-invariant confounders.

A total of 5040 out of 14 021 adults with BPD who received pharmacological treatment and had incident ER admissions due to traumatic injuries from 2001 to 2019 were included. An increased risk of traumatic injuries was found 30 days before treatment [incidence rate ratio (IRR) 4.44 (3.71–5.31), p < 0.0001]. After treatment initiation, the risk remained increased with a smaller magnitude, before returning to baseline [IRR 0.97 (0.88–1.06), p = 0.50] during maintenance treatment. The direct comparison of the risk during treatment to that before and after treatment showed a significant decrease. After treatment cessation, the risk was increased [IRR 1.34 (1.09–1.66), p = 0.006].

This study supports the hypothesis that pharmacological treatment of BPD was associated with a lower risk of ER admissions due to traumatic injuries but an increased risk after treatment cessation. Close monitoring of symptoms relapse is recommended to clinicians and patients if treatment cessation is warranted.

Air dispersal of respiratory viruses other than SARS-CoV-2 has not been systematically reported. The incidence and factors associated with air dispersal of respiratory viruses are largely unknown.

We performed air sampling by collecting 72,000 L of air over 6 hours for pediatric and adolescent patients infected with parainfluenza virus 3 (PIF3), respiratory syncytial virus (RSV), rhinovirus, and adenovirus. The patients were singly or 2-patient cohort isolated in airborne infection isolation rooms (AIIRs) from December 3, 2021, to January 26, 2022. The viral load in nasopharyngeal aspirates (NPA) and air samples were measured. Factors associated with air dispersal were investigated and analyzed.

Of 20 singly isolated patients with median age of 30 months (range, 3 months–15 years), 7 (35%) had air dispersal of the viruses compatible with their NPA results. These included 4 (40%) of 10 PIF3-infected patients, 2 (66%) of 3 RSV-infected patients, and 1 (50%) of 2 adenovirus-infected patients. The mean viral load in their room air sample was 1.58×103 copies/mL. Compared with 13 patients (65%) without air dispersal, these 7 patients had a significantly higher mean viral load in their NPA specimens (6.15×107 copies/mL vs 1.61×105 copies/mL; P < .001). Another 14 patients were placed in cohorts as 7 pairs infected with the same virus (PIF3, 2 pairs; RSV, 3 pairs; rhinovirus, 1 pair; and adenovirus, 1 pair) in double-bed AIIRs, all of which had air dispersal. The mean room air viral load in 2-patient cohorts was significantly higher than in rooms of singly isolated patients (1.02×104 copies/mL vs 1.58×103 copies/mL; P = .020).

Air dispersal of common respiratory viruses may have infection prevention and public health implications.