About one-third of South African women have clinically significant symptoms of postpartum depression (PPD). Several socio-demographic risk factors for PPD exist, but data on medical and obstetric risk factors remain scarce for low- and middle-income countries and particularly in sub-Saharan Africa. We aimed to estimate the proportion of women with PPD and investigate socio-demographic, medical and obstetric risk factors for PPD among women receiving private medical care in South Africa (SA).

In this longitudinal cohort study, we analysed reimbursement claims from beneficiaries of an SA medical insurance scheme who delivered a child between 2011 and 2020. PPD was defined as a new International Classification of Diseases, 10th Revision diagnosis of depression within 365 days postpartum. We estimated the frequency of women with a diagnosis of PPD. We explored several medical and obstetric risk factors for PPD, including pre-existing conditions, such as HIV and polycystic ovary syndrome, and conditions diagnosed during pregnancy and labour, such as gestational diabetes, pre-term delivery and postpartum haemorrhage. Using a multivariable modified Poisson model, we estimated adjusted risk ratios (aRRs) and 95% confidence intervals (CIs) for factors associated with PPD.

Of the 47,697 participants, 2,380 (5.0%) were diagnosed with PPD. The cumulative incidence of PPD increased from 0.8% (95% CI 0.7–0.9) at 6 weeks to 5.5% (5.3–5.7) at 12 months postpartum. PPD risk was higher in individuals with history of depression (aRR 3.47, 95% CI [3.14–3.85]), preterm delivery (1.47 [1.30–1.66]), PCOS (1.37 [1.09–1.72]), hyperemesis gravidarum (1.32 [1.11–1.57]), gestational hypertension (1.30 [1.03–1.66]) and postpartum haemorrhage (1.29 [0.91–1.85]). Endometriosis, HIV, gestational diabetes, foetal stress, perineal laceration, elective or emergency C-section and preeclampsia were not associated with a higher risk of PPD.

The PPD diagnosis rate was lower than anticipated, based on the PPD prevalence of previous studies, indicating a potential diagnostic gap in SA’s private sector. Identified risk factors could inform targeted PPD screening strategies.

Social determinants of health (SDHs) exert a significant influence on various health outcomes and disparities. This study aimed to explore the associations between combined SDHs and mortality, as well as adverse health outcomes among adults with depression.

The research included 48,897 participants with depression from the UK Biobank and 7,771 from the US National Health and Nutrition Examination Survey (NHANES). By calculating combined SDH scores based on 14 SDHs in the UK Biobank and 9 in the US NHANES, participants were categorized into favourable, medium and unfavourable SDH groups through tertiles. Cox regression models were used to evaluate the impact of combined SDHs on mortality (all-cause, cardiovascular disease [CVD] and cancer) in both cohorts, as well as incidences of CVD, cancer and dementia in the UK Biobank.

In the fully adjusted models, compared to the favourable SDH group, the hazard ratios for all-cause mortality were 1.81 (95% CI: 1.60–2.04) in the unfavourable SDH group in the UK Biobank cohort; 1.61 (95% CI: 1.31–1.98) in the medium SDH group and 2.19 (95% CI: 1.78–2.68) in the unfavourable SDH group in the US NHANES cohort. Moreover, higher levels of unfavourable SDHs were associated with increased mortality risk from CVD and cancer. Regarding disease incidence, they were significantly linked to higher incidences of CVD and dementia but not cancer in the UK Biobank.

Combined unfavourable SDHs were associated with elevated risks of mortality and adverse health outcomes among adults with depression, which suggested that assessing the combined impact of SDHs could serve as a key strategy in preventing and managing depression, ultimately helping to reduce the burden of disease.

The Diagnostic and Statistical Manual of Mental Disorders – 5th Edition (DSM-5) and International Classification of Diseases – 11th Revision (ICD-11) employ different post-traumatic stress disorder (PTSD) criteria, necessitating updated prevalence estimates. Most of the existing evidence is still based on ICD-Tenth Revision and DSM-Fourth Edition criteria, leading to varied estimates across populations. This study provides current PTSD prevalence rates in the German general population, comparing DSM-5 and ICD-11 criteria and examines variations by age and gender.

In a 2016 cross-sectional survey of 2404 adults (18–94 years) representative of the German general population, participants completed the Life-Events-Checklist for DSM-5 (LEC-5) for trauma exposure and the PTSD Checklist for DSM-5 (PCL-5) for PTSD symptoms. Probable PTSD diagnoses were based on DSM-5-, ICD-11-algorithms and suggested cut-off scores. Chi-square and McNemar’s tests were used to test differences in prevalence rates by diagnostic framework, age and gender.

Of the total sample, 47.2% (n = 1135) reported experiencing at least one lifetime traumatic event (TE), with transportation accidents (7.3%) and life-threatening injuries (4.9%) being most common. Probable PTSD prevalence was 4.7% under both DSM-5 and ICD-11 criteria, and 2.6% based on a conservative cut-off normed for prevalence estimation. Gender and age were not significantly associated with TE exposure or PTSD prevalence, though trauma types varied: female participants more often reported sexual violence and severe suffering, while more male participants reported physical assaults and various types of accidents. DSM-5 and ICD-11 diagnostic algorithms had substantial yet not perfect agreement (κ = 0.62). Particularly within the re-experiencing symptoms, cluster agreement was only moderate (κ = 0.57). The cut-off method aligned more closely with DSM-5 (κ = 0.60) than ICD-11 algorithm (κ = 0.42).

This study provides updated PTSD prevalence estimates for the German general population and underscores differences between DSM-5 and ICD-11 in identifying cases, particularly with respect to re-experiencing symptoms. These findings emphasize that while overall PTSD prevalence rates under DSM-5 and ICD-11 criteria are similar, the diagnostic frameworks identify partially distinct cases, reflecting differences in symptom definitions. This highlights the need to carefully consider the impact of evolving diagnostic criteria when interpreting prevalence estimates and comparing results across studies.

The environment shapes the risk of psychosis. In particular, urbanicity, deprivation or inequality, migrant density and cannabis availability may not only influence psychosis incidence, but also the characteristics of individuals who arrive at clinical services. This study examined how socioeconomic factors influence the incidence and characteristics of cases of First-Episode Psychosis (FEP).

We analysed prospective data collected from the FEP early detection programme of Emilia-Romagna, a high-income Italian region. Participants were 1240 individuals aged 18–35 years, who presented at the public healthcare services for a FEP. Exposures were derived from area-level data of 331 municipalities. We used population density, socioeconomic deprivation, educational deprivation, economic inequality, migrant density (proportion of migrants), frequent cannabis use (proportion of people aged 15–19 years old who reported frequent cannabis use). Outcome measures were FEP incidence (cases/100 000 inhabitants at risk per year) and characteristics (age of onset, migrant status, unemployment, substance use, treatment lag [DUP], family and resource problems). We reviewed pertinent literature, and formulated a Directed Acyclic Graph to present causal assumptions and provide adjustment sets for Bayesian spatial and multilevel models of social causation. To compare the effects of different exposures, we computed Average Marginal Effects and report the outcome changes that correspond to one standard deviation change of the exposure, incidence rate ratios (IRR) or odds ratios (OR).

The exposures and incidence of FEP displayed heterogeneous spatial distribution, with no spatially organized pattern. Accordingly, incidence and characteristics were best modelled as non-spatial, three-level hierarchical models. The incidence of FEP was influenced by population density (IRR, 1.14; 95% CrI, 1.03; 1.29), educational deprivation (IRR, 1.15; 95% CrI, 1.02; 1.31) and frequent cannabis use (IRR, 1.31; 95% CrI, 0.98; 1.82), more than socioeconomic deprivation. Higher migrant density in an area shortened the DUP on average by 3.4 months (95% CrI, −1.122; 0.76), while an increase of cannabis use of one standard deviation increased the DUP of 12.9 months (95% CrI, −2.86; 6229). Socioeconomic deprivation increased the likelihood of FEP cases being substance users (OR, 1.12; 95% CrI, 1.01; 1.26), while population density decreased it (OR, 0.91; 95% CrI, 0.83; 1.00).

Area-level socioeconomic features affect both the incidence and the characteristics of FEP, including the probability of individual being migrants, substance users or having a different DUP. Educational deprivation may function as a proxy for culture- or cognitive-related factors. Area-level socioeconomic data may inform public healthcare strategies for early identification and availability of tertiary clinical services.

Suicide rates in the United States have been increasing, necessitating an understanding of demographic variations by ethnicity, age, sex and method to inform effective prevention strategies.

To dissect suicide rates in the US population from 2001 to 2023 by age, sex, ethnicity, and method.

This retrospective observational study utilized suicide data and population statistics from the CDC’s WISQARS database for the years 2001 (n = 30,418), 2018 (n = 48,132), 2020 (n = 45,721) and 2023 (n = 49,014). Cases were stratified by age, sex, ethnicity, and suicide method to assess trends and demographic differences.

From 2001 to 2023, the overall US suicide rate rose from 10.7 to 14.6 per 100,000, with a temporary decrease in 2019 and 2020 (14.4 and 13.8, respectively). The primary driver of the increase was firearm-related suicides among White males, contributing 25.8% of the rise from 2001 to 2018 and 51.6% from 2020 to 2023. Decline between 2018 and 2020 was mainly due to reductions in firearm and drug-related suicides among White males, but firearm suicides surged again from 2020 to 2023. Additionally, firearm suicides among ethnic minorities, especially Black/African-American males, accounted for 14.0% of the increase during 2020–2023. Drug-related suicides also increased by 8.6% among White females aged 45 and older in the same period.

Firearm suicides are the leading factor in the changing suicide rates in the United States from 2001 to 2023, alongside rising drug-related suicides among White females. These trends highlight the necessity for targeted prevention efforts that consider demographic-specific factors and method accessibility.

There is a substantial body of literature on environmental risk associated with schizophrenia. Most research has largely been conducted in Europe and North America, with little representation of the rest of the world; hence generalisability of findings is questionable. For this reason, we performed a mapping review of studies on environmental risk for schizophrenia spectrum disorders, recording the country where they were conducted, and we linked our findings with publicly available data to identify correlates with the uneven global distribution. Our aim was to evaluate how universal is the ‘common knowledge’ of environmental risk for psychosis collating the availability of evidence across different countries and to generate suggestions for future research identifying gaps in evidence.

We performed a systematic search and mapping of studies in the PubMed and PsycINFO electronic databases reporting on exposure to environmental risk for schizophrenia including obstetric complications, paternal age, migration, urbanicity, childhood trauma, and cannabis use and subsequent onset of schizophrenia spectrum disorders. This search focused on articles published from the date of the first available publication until 31 May 2023. We recorded the country where they were conducted. We downloaded publicly available data on population size, measures of wealth, medical provisions, research investment, and of quality research outputs per country and performed regression analyses of each predictor with the number of studies and recruited cases in each country.

We identified 308 publications that included a sample size of 445,000 patients with schizophrenia spectrum disorders. The majority were conducted in northern Europe and North America, with large parts of the world totally unrepresented. In the associations between the number of environmental risk studies for schizophrenia with potential predictors, we found that neither population nor wealth or research investment were strong predictors of research outputs in the field. Interestingly, the stronger correlations were found for number of researchers per population and for indicators of top-end scientific achievements, such as number of Nobel laureates per country.

Our results demonstrate a gap of knowledge due to the underrepresentation of studies on environmental risk of schizophrenia spectrum disorders in large parts of the world. This has implications not only in the generalisability of any findings from research conducted in the Northern hemisphere but also in our ability to progress in efforts to make causal inferences about biological pathways to schizophrenia. These findings reinforce the need to focus research on populations that are underrepresented in research and underserved in health care.

Loneliness is a common public health concern, particularly among mid- to later-life adults. However, its impact on early mortality (deaths occurring before reaching the oldest old age of 85 years) remains underexplored. This study examined the predictive role of loneliness on early mortality across different age groups using data from the Health and Retirement Study (HRS).

A retrospective cohort study was conducted using data from the 2010–2020 waves of the HRS, restricted to participants aged 50–84 years at baseline. Loneliness was measured using the 11-item UCLA Loneliness Scale, categorized into four levels: low/no loneliness (scores 11–13), mild loneliness (14–16), moderate loneliness (17–20) and severe loneliness (21–33). Cox proportional hazards models and time-varying Cox regression models with age as the time scale were created to evaluate the relationship between loneliness and early mortality, adjusting for sociodemographic, lifestyle, and physical and mental health factors.

Among 6,392 participants, the overall mortality rate before the age of 85 years was 19.1 per 1,000 person-years. A dose–response relationship was observed, with moderate and severe loneliness associated with 23% (adjusted hazard ratio [aHR]: 1.23, 95% confidence interval [CI] = 1.02–1.48) and 36% (aHR: 1.36, 95% CI = 1.13–1.65) higher mortality risk, respectively. Significant associations existed for the 65–74-year-old (aHR = 1.37, 95% CI = 1.03–1.83) and 75–84-year-old (aHR = 1.77, 95% CI = 1.23–2.56) age groups in the fully-adjusted models, but not for the 50–64-year-old age group. Time-varying Cox models showed a stronger association for severe loneliness (aHR = 1.65, 95% CI = 1.37–1.99).

Loneliness is a significant predictor of mortality among older adults. Preventive and interventional programs targeting loneliness may promote healthy ageing.

Evidence on the effects of parental Adverse Childhood Experiences (ACEs) on adolescent mental health remains limited. This study investigates the associations between parental ACEs, children’s exposure to threat- and deprivation-related ACEs, and adolescent depression and anxiety using data from the Longitudinal Study of Australian Children.

We conducted a secondary analysis of the Longitudinal Study of Australian Children (LSAC), a population-based longitudinal cohort study. Parental ACEs were retrospectively reported by caregivers. Children’s exposure to ACEs was assessed from ages 4–17 years and categorised as threat-related ACEs (e.g., bullying, hostile parenting, unsafe neighbourhoods, family violence) or deprivation-related ACEs (e.g., financial hardship, parental substance abuse, parental psychological distress, death of a family member, parental separation, parental legal problems). Depressive and anxiety symptoms were self-reported by adolescents at ages between 12 and 17 years. Modified Poisson regression models were used to examine the independent and combined associations of parental ACEs and children’s threat- and deprivation-related ACEs (assessed before ages 12, 14, and 16 years) with depression and anxiety outcomes, including tests for interaction effects.

The analysis included 3,956 children aged 12–13 years, 3,357 children aged 14–15 years, and 3,089 children aged 16–17 years. Males comprised 50.8–59.8% and females 40.2–49.2% across all ages. By the age of 17, 30.4% and 9.4% of the adolescents had depression and anxiety, respectively. Parental ACEs (≥2) were associated with increased depression risk at ages 12 to 13 years (RR = 1.42; 95% CI: 1.10–1.84) and at 16–17 years (RR = 1.19; 95% CI: 1.02–1.39). Exposure to ≥ 2 deprivation-related ACEs significantly increased the risk of depression across all ages, with relative risks ranging from 1.31 to 2.18. High threat-related ACEs (≥2) were associated with increased depression risk only at 12 to 13 years (RR = 2.01; 95% CI: 1.28–3.17). No significant interactions were observed.

The findings reinforce the ACEs model by showing that, at the population level, early identification of children exposed to early life deprivations rooted in financial crisis or familial adversities, combined with targeted interventions for both children and parents and supportive social policies, can reduce long-term mental health risks.

The COVID-19 pandemic exacerbated psychological distress, but limited information is available on the shifts in mental health symptoms and their associated factors across different stages. This study was conducted to more reliably estimate shifts in mental health impacts and to identify factors associated with symptoms at different pandemic stages.

We performed a national repeated cross-sectional study at stable (2021), recurrence (2022), and end-of-emergency (2023) stages based on representative general national population with extensive geographic coverage. Anxiety, depression, post-traumatic stress disorder (PTSD) and insomnia symptoms were evaluated by GAD-7, PHQ-9, IES-R and ISI scales, respectively, and their associated factors were identified via multivariable linear regression.

Generally, 42,000 individuals were recruited, and 36,218, 36,097 and 36,306 eligible participants were included at each stage. The prevalence of anxiety, depression and insomnia symptoms increased from 13.7–16.4% at stable to 17.3–22.2% at recurrence and decreased to 14.5–18.6% at end of emergency, while PTSD symptom continuously increased from 5.1% to 7.6% and 9.2%, respectively (all significant, P < 0.001). Common factors associated with mental health symptoms across all stages included centralized quarantine, frontline work and residence in initially widely infected areas. Centralized quarantine was linked to anxiety, depression, PTSD and insomnia during the stable, recurrence and end-of-emergency stages. Frontline workers exhibited higher risks of anxiety, depression and insomnia throughout these stages. Individuals in initially widely infected areas were more likely to experience depression and PTSD, particularly during the stable and recurrence stages. Stage-specific risk factors were also identified. Lack of outdoor activity was associated with anxiety, depression and insomnia during the stable and recurrence stages. Residents in high-risk areas during the recurrence stage correlated with increased anxiety and insomnia. Suspected infection was tied to anxiety and insomnia in the recurrence and end-of-emergency stages, while the death of family or friends was linked to PTSD during recurrence and to depression, PTSD and insomnia at the end-of-emergency stage.

Mental health symptoms increased when pandemic recurred, and could remain after end-of-emergency, requiring prolonged interventions. Several key factors associated with mental symptoms and their variations were identified at different pandemic stages, suggesting different at-risk populations.

Although the United States incarcerates nearly two million people, the epidemiology of psychiatric disorders in correctional populations is not well understood, and no study has examined temporal trends in psychiatric disorder prevalences within a single correctional system. This study assessed how psychiatric disorder prevalences have changed in the Texas Department of Criminal Justice (TDCJ), the largest American state prison system housing post-conviction, sentenced individuals.

This retrospective cohort study of TDCJ electronic medical record data from 1 January 2016 through 31 December 2023 included all persons incarcerated for any duration during that period. Diagnoses were based on International Classification of Disease (ICD-10) diagnostic codes. Outcomes were annual prevalences of depressive, bipolar and schizophrenia spectrum disorders stratified by age, race and sex. Cochran-Armitage Tests were used to assess temporal trends within each stratum. Two-way interactions were assessed by fitting Generalized Estimating Equations models using autoregressive correlation with repeated subjects.

The overall population ranged from 170,269 to 222,798 individuals. Approximately, one-third were White (34.5–35.4%), one-third Black (31.0–32.3%), and one-third Hispanic (32.7–33.5%). Most were aged 30–49 (52.8–57.3%), and men (88.9–90.7%) outnumbered women (9.3–11.1%). The prevalences (per 100 [95% CI]) of psychiatric disorders generally increased when comparing 2016 to 2023. Depressive disorders increased the most among those aged 30–49 (5.23 [5.10–5.35] to 6.71 [6.56–6.86]), Hispanic individuals (3.86 [3.72–4.00] to 5.72 [5.53–5.90]), and men (4.72 [4.63–4.82] to 6.53 [6.42–6.65]). Bipolar disorders increased the most among those aged ≥50 (2.57 [2.42–2.72] to 3.46 [3.29–3.63]), Hispanic individuals (1.31 [1.23–1.40] to 2.23 [2.11–2.35]), and men (2.26 [2.20–2.33] to 3.12 [3.04–3.20]). Schizophrenia spectrum disorders increased the most among those aged ≤29 (1.33 [1.24–1.42] to 2.52 [2.35–2.68]), Hispanic individuals (1.53 [1.44–1.62] to 3.21 [3.35–4.40]), and women (1.27 [1.56–1.89] to 4.24 [3.95–4.53]). When stratified by demographic variables, trend tests were significant for nearly all comparisons (P < 0.0001), and all two-way interactions were significant (P < 0.0001).

The prevalences of major psychiatric disorders in the Texas prison system increased when comparing 2016 to 2023, with certain disorders rising more rapidly than others within specific subgroups. These findings emphasize the need for expanded mental health treatment options and resources within correctional settings.

The epidemiology and age-specific patterns of lifetime suicide attempts (LSA) in China remain unclear. We aimed to examine age-specific prevalence and predictors of LSA among Chinese adults using machine learning (ML).

We analyzed 25,047 adults in the 2024 Psychology and Behavior Investigation of Chinese Residents (PBICR-2024), stratified into three age groups (18–24, 25–44, ≥ 45 years). Thirty-seven candidate predictors across six domains—sociodemographic, physical health, mental health, lifestyle, social environment, and self-injury/suicide history—were assessed. Five ML models—random forest, logistic regression, support vector machine (SVM), Extreme Gradient Boosting (XGBoost), and Naive Bayes—were compared. SHapley Additive exPlanations (SHAP) were used to quantify feature importance.

The overall prevalence of LSA was 4.57% (1,145/25,047), with significant age differences: 8.10% in young adults (18–24), 4.67% in adults aged 25–44, and 2.67% in older adults (≥45). SVM achieved the best test-set performance across all ages [area under the curve (AUC) 0.88–0.94, sensitivity 0.79–0.87, specificity 0.81–0.88], showing superior calibration and net clinical benefit. SHAP analysis identified both shared and age-specific predictors. Suicidal ideation, adverse childhood experiences, and suicide disclosure were consistent top predictors across all ages. Sleep disturbances and anxiety symptoms stood out in young adults; marital status, living alone, and perceived stress in mid-life; and functional limitations, poor sleep, and depressive symptoms in older adults.

LSA prevalence in Chinese adults is relatively high, with a clear age gradient peaking in young adulthood. Risk profiles revealed both shared and age-specific predictors, reflecting distinct life-stage vulnerabilities. These findings support age-tailored suicide prevention strategies in China.

This study employs a longitudinal network approach to investigate the dynamic relationships between COVID-19-related stressors and depressive symptoms among Canadian adults and to explore any sex and age differences in these associations.

The study utilised data from the Canadian Longitudinal Study on Ageing (CLSA), a large, national, long-term study of Canadian adults aged 45 years and older. Depressive symptoms were measured using the Centre for Epidemiologic Studies Depression Scale (CES-D), and COVID-19-related stressors were evaluated using a standardised stress inventory adapted for the pandemic context. The cross-lagged panel network analysis (CLPN) was employed to examine the temporal relationships and dynamic interactions between depressive symptoms and COVID-19-related stressors.

Significant variations in network structures and strengths were identified across demographic groups. Individuals aged between 45 and 65 years and females exhibited stronger connections between COVID-19-related stressors and depressive symptoms. Central symptoms such as “feeling unhappy” were consistent across groups, while “feeling depressed” was more central among males and “increased verbal or physical conflict” among females. Additionally, health-related stressors and family separation emerged as critical bridge symptoms for males and individuals under 65 years, respectively.

Both cross-sectional and longitudinal relationships, and directionality between COVID-19-related stressors and depressive symptoms across sex and age groups were identified. The findings of the study highlight that dedicated mental health intervention and prevention efforts are warranted to ameliorate the negative impact of stressors on depressive symptoms.

Promoting mental health, preventing mental disorders and providing effective treatments are public health priorities. Randomized controlled trials (RCTs) frequently evaluate mental health and psychosocial support interventions to achieve one or more of these objectives. Distinguishing between RCTs focused on mental health promotion, prevention or treatment remains conceptually and methodologically challenging. No standardized tool exists to position RCTs along a promotion-to-treatment continuum in mental health. We aimed to develop and validate the VErona-LUgano Tool (VELUT) for distinguishing RCTs along the promotion-to-treatment continuum.

An interdisciplinary tool development group (TDG) was established. The Population, Intervention, Comparison and Outcome framework was used to define key constructs. Items in the tool were devised, categorized and reduced through qualitative and quantitative methods. Finally, we performed a preliminary validation of the VELUT applying item response theory (IRT) using data from 180 RCTs.

The TDG generated 33 items for the initial version of the VELUT, reduced to 16 through review, cognitive interviews and psychometric analysis. Analyses of 180 RCTs using the 16-item tool showed high internal consistency (α = 0.94) and unidimensionality. Following item reduction and IRT, a final 8-item version was retained, and IRT models confirmed strong item discrimination for the 8 items and high scale reliability (marginal reliability >0.90 across most of the range of the scale), good response distribution, item performance and alignment with the Institute of Medicine (IOM) promotion-to-treatment continuum.

The VELUT addresses methodological gaps in global mental health research by helping to position RCTs of MHPSS interventions along the IOM promotion-to-treatment continuum.

Fine particulate matter (PM2.5) exposure and unfavourable lifestyle are both significant risk factors for mental health disorders, yet their combined effects on adolescent depression and anxiety remain poorly understood. This study aims to determine whether PM2.5 exposure and lifestyle are independently associated with adolescent depression and anxiety, and whether there are joint effects between these factors on mental health outcomes.

In this cross-sectional study, 19852 participants were analysed. PM2.5 concentrations were obtained from the ChinaHighAirPollutants (CHAP) dataset. Lifestyle factors were assessed through self-reported questionnaires, and a healthy lifestyle score was developed based on eight lifestyle risk factors. Depression and anxiety were assessed using the PHQ-9 and GAD-7 scales. Restricted cubic spline analysed dose–response relationships between PM2.5 exposure and mental health outcomes. The independent and joint effects were assessed using logistic regression models. Both multiplicative and additive interactions (relative excess risk due to interaction, RERI) were examined. Multiple classification approaches were incorporated to ensure robust results.

The study included 19852 participants with a mean age of 15.16 years (SD 1.60), comprising 9886 (49.8%) males and 9966 (50.2%) females. Depression and anxiety were identified in 3845 (19.37%) and 3230 (16.27%) participants, respectively. PM2.5 exposure showed a linear dose-response relationship with depression and anxiety. Joint effects analysis at the 75th percentile of PM2.5 with a lifestyle risk score of 4 revealed the strongest associations, with adjusted odds ratios of 4.49 (95% CI: 3.79–5.33) for depression, 4.01 (95% CI: 3.36–4.78) for anxiety and 4.24 (95% CI: 3.52–5.10) for their comorbidity. Simultaneously, significant additive interactions (RERI > 0) between high levels of PM2.5 exposure and unfavourable lifestyle factors were detected, suggesting synergistic effects on mental health outcomes. Subgroup and sensitivity analyses confirmed the robustness of these findings.

High PM2.5 exposure and unfavourable lifestyle factors demonstrated significant independent and joint effects on depression and anxiety among adolescents. These findings highlight that implementing stringent air pollution control measures, combined with promoting healthy lifestyle practices, may be crucial for protecting adolescent mental health.

Depression among middle-aged and older adults is a critical public health priority. Clarifying the dynamic evolution of depression is essential for establishing prevention and intervention strategies; however, relevant research is limited. The aim of this study was to elucidate the transition patterns underlying different depressive symptoms (DS) states.

Data from the China Health and Retirement Longitudinal Study were utilised in this study, which included participants aged ≥45 years with multiple DS assessments via the Center for Epidemiological Studies Depression Scale. Multi-state Markov models were employed to estimate transition probabilities and intensities between DS states, the total length of stay and mean sojourn time in each state and the hazard ratios (HRs) of factors.

Among 19,991 participants (average follow-up: 7.3 years), the 10-year cumulative probabilities of transition from non-DS to depressive states increased by 19.4% in males and 31.8% in females. Mild DS was the most unstable state, with the highest transition intensities (males: 1.029; females: 0.970) and shortest sojourn time (males: 0.959 years; females: 1.022 years). Sex and age strongly influenced depressive state transitions. Compared to participants without chronic disease, those with ≥3 chronic diseases had a higher risk of developing mild DS (HR = 1.685, 95% Confidence Interval [CI]: 1.530–1.856) and transitioning to death from both the non-DS (HR = 2.905, 95% CI: 2.293–3.681) and severe-DS (HR = 3.429, 95% CI: 1.290–9.112) states, but a lower likelihood of recovery from mild DS (HR = 0.821, 95% CI: 0.749–0.900) and severe DS (HR = 0.730, 95% CI: 0.630–0.847). Compared to no participation in social activities, frequent participation was associated with a lower risk of progression to the mild-DS state (HR = 0.851, 95% CI: 0.785–0.920) and a greater likelihood of recovery from severe DS (HR = 1.169, 95% CI: 1.034–1.322). Being underweight was associated with an increased risk of mild-DS onset (HR = 1.338, 95% CI: 1.129–1.587) and transitioning to death from both the non-DS and mild-DS states, compared with individuals of normal weight.

Our study revealed a continuous population shift towards depressive states and identified the mild-DS state as a critical intervention state owing to its instability. In addition to sex and age, modifiable factors, including chronic disease conditions, social activity participation and weight status, significantly influenced DS-state transitions, offering actionable insights for precision prevention strategies.

While depressive symptoms are common during menopausal transition, the relationship between the two remains unclear. Therefore, this study aimed to examine the longitudinal changes in depressive symptoms among middle-aged Korean women and identify those with elevated and worsening symptoms during this period.

A total of 1,178 participants who underwent comprehensive health examinations at Kangbuk Samsung Hospital in Korea were followed for a median of 10.8 years (IQR, 9.2–11.6; maximum, 12.7), including all women who reached natural menopause during follow-up, with only data prior to HRT initiation included. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D), and menopausal stages were classified according to the STRAW + 10 criteria and final menstrual period (FMP). Linear mixed-effects models and group-based trajectory modelling (GBTM) were applied to evaluate longitudinal changes in depressive symptoms and to identify distinct trajectories in the severity and stability of depressive symptoms.

The age-adjusted prevalence of CES-D ≥ 16 was 11.0%, 11.5%, 11.2% and 12.4%, with corresponding mean scores of 6.7, 6.6, 6.9 and 7.1 across stages. After adjusting for time-varying age and covariates, menopausal stage transitions were not significantly associated with higher levels of depressive symptoms, whether analysed as continuous or binary variables. For binary CES-D (≥16), the estimated coefficients (95% CI) were 0.10 (–0.20 to 0.41) for early transition, 0.09 (–0.21 to 0.39) for late transition and 0.26 (–0.09 to 0.61) for post-menopause. Similarly, time relative to the FMP (–11 to +9 years) showed no significant association with depressive symptoms. GBTM identified three distinct trajectories: most participants (75.5%) maintained consistently low depressive symptoms throughout the transition, whereas 5.8% showed worsening symptoms. Poor sleep quality (OR 5.83, 95% CI 3.25 to 10.45) and moderate-to-severe vasomotor symptoms (OR 2.95, 95% CI 1.30 to 6.70) were significantly associated with the worsening trajectory. Suicidal ideation was higher in this group (45.4% at baseline, increasing to 70.5% at follow-up).

Most women maintained low depressive symptoms during the menopausal transition; however, a subset experienced worsening symptoms linked to menopause-related physical symptoms. Medical visits for menopause-related symptoms may provide opportunities for screening depressive symptoms in higher-risk women, though the screening effectiveness requires further evaluation.

To investigate the association of midlife and late-life undiagnosed mood symptoms, especially their comorbidity, with long-term dementia risk among multi-regional and ethnic adults.

The prospective study used data from the UK Biobank (N = 142,670; mean follow-up 11.0 years) and three Asian studies (N = 1,610; mean follow-up 4.4 years). Undiagnosed mood symptoms (manic symptoms, depressive symptoms and comorbidity of depressive and manic symptoms) and diagnosed mood disorders (depression, mania and bipolar disorders) were classified. Plasma levels of 168 metabolites were measured. The association between undiagnosed mood symptoms and 12-year dementia (including subtypes) risk and domain-specific cognitive function was examined. The contribution of metabolites in explaining the association between symptom comorbidity and dementia risk was estimated.

Undiagnosed mood symptoms were prevalent (11.4% in the UK cohort and 31.2% in Asian cohorts) among 1,462 (1.0%) and 74 (19.4%) participants who developed dementia. Comorbidity of undiagnosed mood symptoms was associated with higher dementia risk (sub-distribution hazard ratios = 9.46; 95% confidence interval = 4.07–21.97), especially Alzheimer’s disease, and with worse reasoning ability, poorer numeric memory and metabolic dysfunction. Glucose and total Esterified Cholesterol explained 9.1% of the association between symptom comorbidity and dementia, with most of the contribution being from glucose (6.8%).

Comorbidity of undiagnosed mood symptoms was associated with a higher cumulative risk of dementia in the long term. Glucose metabolism could be implicated in the development of mood disorders and dementia. The distinctive pathophysiological mechanism between psychiatric and neurodegenerative disorders warrants further exploration.

Efforts to reduce restrictive practices (RPs) in mental health care are growing internationally. Yet, inconsistent definitions and perspectives often challenge the consistent implementation and evaluation of reduction strategies. This study explored which scenarios different mental health stakeholders classify as RPs, examined the contextual factors influencing these classifications and compared classification patterns across clinicians, researchers, service users and family caregivers.

An international cross-sectional survey was conducted using a multilingual online questionnaire hosted on the Qualtrics platform. A total of 851 stakeholders participated, including clinicians (n = 517), service users (n = 80), family caregivers (n = 89) and researchers (n = 165). Participants were presented with 44 potential RP case scenarios and asked to rate whether each scenario should be classified as an RP using a four-point Likert scale (Definitely yes, Probably yes, Probably no, Definitely no). The scenarios were organized into 22 paired comparisons, each sharing the same core context but differing in specific details. Paired comparisons were analyzed one pair at a time, allowing us to identify classification patterns between the scenarios and isolate the effects of particular contextual factors using ordered logistic regression. Interaction analyses were then conducted to assess how classification patterns varied across stakeholder groups.

Substantial discrepancies exist both within and between stakeholder groups regarding whether a given action should be considered an RP or not. Physically visible actions were often identified as RPs across all groups, while less visible forms often went unrecognized. Contextual differences, such as the healthcare professional’s intention, duration of the action, methods used, presence or absence of consent, door-locking status, and the severity of anticipated harm to be prevented influenced whether a given action was classified as an RP. Service users classified more scenarios as RPs than other groups; however, their decisions were more context-sensitive, shifting notably even with minor changes in scenario details. Among the 22 paired scenarios compared, 13 (59.09%) showed significant differences (p < 0.01) within at least one stakeholder group and eight demonstrated differences between groups.

Mental health stakeholders’ interpretations of RPs were often shaped not only by the inherent coercive nature of actions but also by the context in which they occurred and the professional role of the assessors. This underscores the need for harmonized definitions and classification frameworks for RPs, co-designed with diverse stakeholders. Addressing less visible forms of RPs in policy and clinical practice is also essential.

In England, psychological therapies provided in primary care are recommended as first-line treatment for people living with mild-to-moderate dementia experiencing depression or anxiety. It is known that people living with dementia experience more barriers to accessing therapy than people without dementia, but such inequalities in terms of rates of access to primary care services are yet to be characterised.

In this retrospective, observational study of linked electronic healthcare records, the national database of the National Health Service (NHS) Talking Therapies for anxiety and depression programme was used to compare pathways to accessing therapy between 6623 people living with dementia and 4 825 489 without dementia between 2012 and 2019. Outcomes included access to an assessment, to therapy and reasons for discharge. Primary analyses used a propensity-score matched cohort to compare outcomes. Exact matching was used for the NHS service entity.

The prevalence of dementia in the study cohort was lower than the prevalence of dementia in a representative population, based on an estimation of prevalence in people with mild-to-moderate age over 35 (0.23% in our study vs 3.82% in previous research). Compared to people without dementia, people living with dementia were less likely to access an assessment (odds ratio [OR] = 0.60; 95% confidence interval [CI]: 0.55–0.65), to subsequently receive therapy (OR = 0.67; 95% CI: 0.61–0.73) and more likely to be discharged because services were deemed not suitable before having an assessment (relative rate ratio [RRR] = 4.90; 95% CI: 4.20–5.72) and starting therapy (RRR = 2.74; 95% CI: 2.24–3.35). Female gender, social deprivation, Asian ethnicity and less common dementia subtypes (such as frontotemporal dementia) were also associated with poorer access rates and a higher likelihood of services being deemed not suitable. Involvement of care partners in the referral process was associated with better access rates.

Pathways to accessing primary care psychological therapy services must be made more accessible for people living with dementia. Better access could be achieved by increasing referrer awareness and training for staff within services to promote access for people living with dementia (especially for groups under-represented in services), better understanding how to involve care partners in the process, as well as when specialist support might be more suited in secondary care. More granularity in the medical coding of rarer dementia diagnoses in electronic health records would also allow for better statistically powered research for these groups.