The heterogeneity of chronic post-COVID neuropsychiatric symptoms (PCNPS), especially after infection by the Omicron strain, has not been adequately explored.

To explore the clustering pattern of chronic PCNPS in a cohort of patients having their first COVID infection during the ‘Omicron wave’ and discover phenotypes of patients based on their symptoms’ patterns using a pre-registered protocol.

We assessed 1205 eligible subjects in Hong Kong using app-based questionnaires and cognitive tasks.

Partial network analysis of chronic PCNPS in this cohort produced two major symptom clusters (cognitive complaint–fatigue and anxiety–depression) and a minor headache–dizziness cluster, like our pre-Omicron cohort. Participants with high numbers of symptoms could be further grouped into two distinct phenotypes: a cognitive complaint–fatigue predominant phenotype and another with symptoms across multiple clusters. Multiple logistic regression showed that both phenotypes were predicted by the level of pre-infection deprivation (adjusted P-values of 0.025 and 0.0054, respectively). The severity of acute COVID (adjusted P = 0.023) and the number of pre-existing medical conditions predicted only the cognitive complaint–fatigue predominant phenotype (adjusted P = 0.003), and past suicidal ideas predicted only the symptoms across multiple clusters phenotype (adjusted P < 0.001). Pre-infection vaccination status did not predict either phenotype.

Our findings suggest that we should pursue a phenotype-driven approach with holistic biopsychosocial perspectives in disentangling the heterogeneity under the umbrella of chronic PCNPS. Management of patients complaining of chronic PCNPS should be stratified according to their phenotypes. Clinicians should recognise that depression and anxiety cannot explain all chronic post-COVID cognitive symptoms.

Adolescents with depression have distinct affective reactions to daily events, but current research is controversial. The emotional context insensitivity theory suggests blunted reactivity in depression, whereas the hypotheses of negative potentiation and mood brightening effect suggest otherwise. While nonlinear associations between depression severity and affective reactivity have been observed, studies with a separate subclinical group remain rare. Subthreshold depression (SD), defined by two to four symptoms lasting for two weeks or more, provides a dimensional view to the underpinnings of affective reactivity. In this study, we compared positive affect (PA) and negative affect (NA) reactivity to positive and negative daily events (uplifts and stress) among adolescents with Major Depressive Disorder (MDD), SD and healthy controls (HC) using experience sampling methods (ESM).

We hypothesized a stepped difference in affective reactivity along the depression spectrum: the MDD group will have the strongest reactivity of PA and NA to uplifts and stress, followed by SD and HC.

Three groups (MDD, SD, and HC) of adolescents were recruited from an epidemiologic sample entitled ‘Hong Kong Child and Adolescent Psychiatric Epidemiologic Survey: Age 6 to 17’. Group status was determined by the Diagnostic Interview Schedule for Children Version 5. They completed an experience sampling diary on smartphone for 14 consecutive days, with 5-10 entries per day. Momentary levels of PA (happy, relaxed, contented), NA (irritated, low, nervous), uplifts and stress experienced before the entry were measured on a 1-7 Likert scale.

The sample consisted of 19 adolescents with MDD, 30 with SD, and 59 HC. The M:F ratio was 17:19. The age range was 12-18 with a mean of 14.8. The overall ESM completion rate was 46%. The MDD group had the highest levels of stress and NA, and the lowest levels of uplifts and PA, followed by the SD and HC groups respectively (p<0.01). Across groups, levels of PA were positively associated with uplifts and negatively associated with stress, whereas levels of NA were positively associated with stress and negatively associated with uplifts. The Group x Uplift interaction effect on PA was significant, with greater PA reactivity in SD (p<0.01) and MDD (p=0.07) when compared with HC. The Group x Uplift interaction effect on NA was significant, with greater NA reactivity in SD than HC (p<0.01). The Group x Stress interaction effect on PA was significant, with greater PA reactivity in SD than HC (p<0.01) and MDD (p<0.01). The Group x Stress interaction effect with NA is non-significant.

Contrary to our hypothesis, adolescents with SD experienced strongest PA and NA reactivity in uplifts and PA reactivity in stress. It provides evidence towards a nonlinear relationship between severity of depression and affective reactivity.

None Declared

Both impulsivity and compulsivity have been identified as risk factors for problematic use of the internet (PUI). Yet little is known about the relationship between impulsivity, compulsivity and individual PUI symptoms, limiting a more precise understanding of mechanisms underlying PUI.

The current study is the first to use network analysis to (a) examine the unique association among impulsivity, compulsivity and PUI symptoms, and (b) identify the most influential drivers in relation to the PUI symptom community.

We estimated a Gaussian graphical model consisting of five facets of impulsivity, compulsivity and individual PUI symptoms among 370 Australian adults (51.1% female, mean age = 29.8, s.d. = 11.1). Network structure and bridge expected influence were examined to elucidate differential associations among impulsivity, compulsivity and PUI symptoms, as well as identify influential nodes bridging impulsivity, compulsivity and PUI symptoms.

Results revealed that four facets of impulsivity (i.e. negative urgency, positive urgency, lack of premeditation and lack of perseverance) and compulsivity were related to different PUI symptoms. Further, compulsivity and negative urgency were the most influential nodes in relation to the PUI symptom community due to their highest bridge expected influence.

The current findings delineate distinct relationships across impulsivity, compulsivity and PUI, which offer insights into potential mechanistic pathways and targets for future interventions in this space. To realise this potential, future studies are needed to replicate the identified network structure in different populations and determine the directionality of the relationships among impulsivity, compulsivity and PUI symptoms.

There are various ways people cope with life events. One can expect generalized positive or negative outcomes across various life domains, called dispositional optimism. This can be explained by attribution theory: how people explain past events, their causations, and outcomes. Understanding attribution styles is important to help people reframe current circumstances and improve mental wellbeing. Our hypothesis is that people of different religious groups may exhibit various levels of optimism and pessimism based on their values, teachings, and practices. Previous research has found that people of Christian faith, or those with a religious faith in general, look to their religion as a way of coping during life adversities. Certain religious practices such as prayers and Church gatherings have been found to improve mental health through increasing dispositional optimism. While the relationship between religiosity and mental health has been previously examined in different religious populations, there are few studies that focused on comparing this relationship across religions.

The objective of this scoping review is to understand the link between religiosity and mental health, focusing primarily on how people of the Christian religion demonstrate dispositional optimism or pessimism when coping with adverse life events, compared to other religious groups or atheists.

This scoping review included original peer reviewed study articles that studied mental health in terms of dispositional optimism or pessimism in people of Christian religion compared to other religious groups. This review used online databases, Ovid MEDLINE and PsycInfo, and used extraction tables to analyze the results of past research.

The results of this scoping review revealed that people of Christian religion, especially those high in religiosity, use their religion as a method of coping. This population also showed higher dispositional optimism compared to atheists or those that believe in other religions. However, when compared to other religions such as Buddhism and Muslim, Christian populations showed lower dispositional optimism.

It is evident that religious involvement is linked to aspects of mental health, but comparing the effects of different religions is still a topic of exploration that can be investigated further to allow deeper understanding of their similarities and differences, as well as the mechanisms by which religion can affect mental health. In this review, a gap in the body of knowledge regarding the relationship between religion and pessimism was revealed. Future research directions could include examining whether dispositional pessimism varies across religious groups, as it does not necessarily have a perfectly inverse relationship with optimism.

None Declared

Despite an elevated risk of psychopathology stemming from COVID-19-related stress, many essential workers stigmatise and avoid psychiatric care. This randomised controlled trial was designed to compare five versions of a social-contact-based brief video intervention for essential workers, differing by protagonist gender and race/ethnicity.

We examined intervention efficacy on treatment-related stigma (‘stigma’) and openness to seeking treatment (‘openness’), especially among workers who had not received prior mental healthcare. We assessed effectiveness and whether viewer/protagonist demographic concordance heightened effectiveness.

Essential workers (N = 2734) randomly viewed a control video or brief video of an actor portraying an essential worker describing hardships, COVID-related anxiety and depression, and psychotherapy benefits. Five video versions (Black/Latinx/White and male/female) followed an identical 3 min script. Half the intervention group participants rewatched their video 14 days later. Stigma and openness were assessed at baseline, post-intervention, and at 14- and 30-day follow-ups. Trial registration: NCT04964570.

All video intervention groups reported immediately decreased stigma (P < 0.0001; Cohen's d = 0.10) and increased openness (P < 0.0001; d = 0.23). The initial increase in openness was largely maintained in the repeated-video group at day 14 (P < 0.0001; d = 0.18), particularly among viewers without history of psychiatric treatment (P < 0.0001; d = 0.32). Increases were not sustained at follow-up. Female participants viewing a female protagonist and Black participants viewing a Black protagonist demonstrated greater openness than other demographic pairings.

Brief video-based interventions improved immediate stigma and openness. Greater effects among female and Black individuals viewing demographically matched protagonists emphasise the value of tailored interventions, especially for socially oppressed groups. This easily disseminated intervention may proactively increase care-seeking, encouraging treatment among workers in need. Future studies should examine intervention mechanisms and whether linking referrals to psychiatric services generates treatment-seeking.

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Late-life depression has substantial impacts on individuals, families and society. Knowledge gaps remain in estimating the economic impacts associated with late-life depression by symptom severity, which has implications for resource prioritisation and research design (such as in modelling). This study examined the incremental health and social care expenditure of depressive symptoms by severity.

We analysed data collected from 2707 older adults aged 60 years and over in Hong Kong. The Patient Health Questionnaire-9 (PHQ-9) and the Client Service Receipt Inventory were used, respectively, to measure depressive symptoms and service utilisation as a basis for calculating care expenditure. Two-part models were used to estimate the incremental expenditure associated with symptom severity over 1 year.

The average PHQ-9 score was 6.3 (standard deviation, s.d. = 4.0). The percentages of respondents with mild, moderate and moderately severe symptoms and non-depressed were 51.8%, 13.5%, 3.7% and 31.0%, respectively. Overall, the moderately severe group generated the largest average incremental expenditure (US$5886; 95% CI 1126–10 647 or a 272% increase), followed by the mild group (US$3849; 95% CI 2520–5177 or a 176% increase) and the moderate group (US$1843; 95% CI 854–2831, or 85% increase). Non-psychiatric healthcare was the main cost component in a mild symptom group, after controlling for other chronic conditions and covariates. The average incremental association between PHQ-9 score and overall care expenditure peaked at PHQ-9 score of 4 (US$691; 95% CI 444–939), then gradually fell to negative between scores of 12 (US$ - 35; 95% CI - 530 to 460) and 19 (US$ -171; 95% CI - 417 to 76) and soared to positive and rebounded at the score of 23 (US$601; 95% CI -1652 to 2854).

The association between depressive symptoms and care expenditure is stronger among older adults with mild and moderately severe symptoms. Older adults with the same symptom severity have different care utilisation and expenditure patterns. Non-psychiatric healthcare is the major cost element. These findings inform ways to optimise policy efforts to improve the financial sustainability of health and long-term care systems, including the involvement of primary care physicians and other geriatric healthcare providers in preventing and treating depression among older adults and related budgeting and accounting issues across services.