Prolonged childhood and adolescent loneliness (CAL) is linked to various adverse mental health outcomes, yet its impact on schizophrenia spectrum disorders (SSD) has been understudied. While loneliness is associated with psychosis and worsens symptoms in SSD, few studies have explored the long-term effects of early loneliness on SSD risk. Understanding how CAL interacts with genetic liability to schizophrenia is essential for identification of high-risk individuals.

This study evaluated whether prolonged CAL is associated with increased SSD risk and examined the interaction between CAL and genetic liability for schizophrenia. Gender differences in these associations were also explored.

Data from the European Gene–Environment Interactions in Schizophrenia (EU-GEI) study were analysed, including 1261 individuals with SSD, 1282 unaffected siblings and 1525 healthy controls. CAL was retrospectively assessed for periods before age 12 years and age 12–16 years. Genetic risk was measured using polygenic risk scores for schizophrenia. Logistic regression models and the Relative Excess Risk due to Interaction (RERI) method were used to examine gene–environment interactions, with stratification by gender.

Prolonged CAL was associated with higher odds of SSD (odds ratio [95% CI] = 5.20 [3.85−7.01] for loneliness before age 12; odds ratio [95% CI] = 7.26 [5.63−9.38] for loneliness during adolescence). The interaction between CAL and genetic risk was strongest during adolescence (RERI [95% CI] = 23.46 [10.75−53.53]). Females showed a greater effect (odds ratio [95 %CI] = 10.04 [6.80−14.94]) than males (odds ratio [95% CI] = 5.50 [3.95−7.66]). Incorporating CAL and genetic interaction increased predictive values to 17% for SSD risk − rising to 22.5% in females − compared with 2.6 and 2.8%, respectively, for genetic risk alone.

Prolonged CAL significantly increases SSD risk, particularly in females. The inclusion of CAL alongside genetic risk substantially enhances predictive accuracy. Early identification of CAL could inform preventive strategies, especially in genetically vulnerable populations.

Studies conducted during the COVID-19 pandemic found high occurrence of suicidal thoughts and behaviours (STBs) among healthcare workers (HCWs). The current study aimed to (1) develop a machine learning-based prediction model for future STBs using data from a large prospective cohort of Spanish HCWs and (2) identify the most important variables in terms of contribution to the model’s predictive accuracy.

This is a prospective, multicentre cohort study of Spanish HCWs active during the COVID-19 pandemic. A total of 8,996 HCWs participated in the web-based baseline survey (May–July 2020) and 4,809 in the 4-month follow-up survey. A total of 219 predictor variables were derived from the baseline survey. The outcome variable was any STB at the 4-month follow-up. Variable selection was done using an L1 regularized linear Support Vector Classifier (SVC). A random forest model with 5-fold cross-validation was developed, in which the Synthetic Minority Oversampling Technique (SMOTE) and undersampling of the majority class balancing techniques were tested. The model was evaluated by the area under the Receiver Operating Characteristic (AUROC) curve and the area under the precision–recall curve. Shapley’s additive explanatory values (SHAP values) were used to evaluate the overall contribution of each variable to the prediction of future STBs. Results were obtained separately by gender.

The prevalence of STBs in HCWs at the 4-month follow-up was 7.9% (women = 7.8%, men = 8.2%). Thirty-four variables were selected by the L1 regularized linear SVC. The best results were obtained without data balancing techniques: AUROC = 0.87 (0.86 for women and 0.87 for men) and area under the precision–recall curve = 0.50 (0.55 for women and 0.45 for men). Based on SHAP values, the most important baseline predictors for any STB at the 4-month follow-up were the presence of passive suicidal ideation, the number of days in the past 30 days with passive or active suicidal ideation, the number of days in the past 30 days with binge eating episodes, the number of panic attacks (women only) and the frequency of intrusive thoughts (men only).

Machine learning-based prediction models for STBs in HCWs during the COVID-19 pandemic trained on web-based survey data present high discrimination and classification capacity. Future clinical implementations of this model could enable the early detection of HCWs at the highest risk for developing adverse mental health outcomes.

NCT04556565

This study aims to outline Clostridioides difficile infection (CDI) trends and outcomes in Mexican healthcare facilities during the COVID-19 pandemic.

Observational study of case series.

Sixteen public hospitals and private academic healthcare institutions across eight states in Mexico from January 2016 to December 2022.

CDI patients.

Demographic, clinical, and laboratory data of CDI patients were obtained from clinical records. Cases were classified as community or healthcare-associated infections, with incidence rates calculated as cases per 10,000 patient days. Risk factors for 30-day all-cause mortality were analyzed by multivariate logistic regression.

We identified 2,356 CDI cases: 2,118 (90%) were healthcare-associated, and 232 (10%) were community-associated. Common comorbidities included hypertension, diabetes, and cancer. Previous high use of proton-pump inhibitors, steroids, and antibiotics was observed. Recurrent infection occurred in 112 (5%) patients, and 30-day mortality in 371 (16%). Risk factors associated with death were a high Charlson score, prior use of steroids, concomitant use of antibiotics, leukopenia, leukocytosis, elevated serum creatine, hypoalbuminemia, septic shock or abdominal sepsis, and SARS-CoV-2 coinfection. The healthcare-associated CDI incidence remained stable at 4.78 cases per 10,000 patient days during the pre-and pandemic periods. However, the incidence was higher in public hospitals.

Our study underscores the need for routine epidemiology surveillance and standardized CDI classification protocols in Mexican institutions. Though CDI rates in our country align with those in some European countries, disparities between public and private healthcare sectors emphasize the importance of targeted interventions.

This study is based on our experience at public hospitals and private clinics of Toledo and Madrid, where we have addressed the treatment of children and adolescents presenting with Eating Disorders (EDs). Our intervention focuses on the application of brief psychotherapy, with particular emphasis on the effectiveness of Eye Movement Desesitization and Reprocessing (EMDR) in these cases.

The primary objective of this study is to determine the benefits of applying EMDR in cases of pediatric and adolescent EDs in comparison to other psychotherapeutic techniques.

Over a period of one year, brief psychotherapy sessions were conducted with children and adolescents diagnosed with EDs. An integrative approach was used, combining family sistemic therapy, cognitive-behavioural therapy techniques, and brief psychodynamic approaches, along with EMDR sessions. Pre and post treatment assessments were conducted to measure changes in symptoms and patients’ quality life.

The results obtained reveal significant improvements in patient symptomatology, including a notable reduction in food-anxiety, dietary restriction and compensatory behaviours. Furthermore, improvements were observed in body image perception and patiends’ overall quality of life. Incidence of relapse cases was minimal.

Our experience suggests that the application of a brief psychotherapy approach, combined with EMDR sessions, can be highly effective in treating children and adolescents with EDs. Early intervention and individualized adaptation of therapies are essential for achieving positive and lasting outcomes in this patient group. These findings underscore the importance of considering integrative approaches in the care of EDs in young population.

None Declared

Depression is a common mental health disorder that often starts during adolescence, with potentially important future consequences including ‘Not in Education, Employment or Training’ (NEET) status.

We took a structured life course modeling approach to examine how depressive symptoms during adolescence might be associated with later NEET status, using a high-quality longitudinal data resource. We considered four plausible life course models: (1) an early adolescent sensitive period model where depressive symptoms in early adolescence are more associated with later NEET status relative to exposure at other stages; (2) a mid adolescent sensitive period model where depressive symptoms during the transition from compulsory education to adult life might be more deleterious regarding NEET status; (3) a late adolescent sensitive period model, meaning that depressive symptoms around the time when most adults have completed their education and started their careers are the most strongly associated with NEET status; and (4) an accumulation of risk model which highlights the importance of chronicity of symptoms.

Our analysis sample included participants with full information on NEET status (N = 3951), and the results supported the accumulation of risk model, showing that the odds of NEET increase by 1.015 (95% CI 1.012–1.019) for an increase of 1 unit in depression at any age between 11 and 24 years.

Given the adverse implications of NEET status, our results emphasize the importance of supporting mental health during adolescence and early adulthood, as well as considering specific needs of young people with re-occurring depressed mood.

The aim was to develop a lucidity measure for use with front-line caregivers to describe lucidity episodes among individuals with dementia, neurological and other illnesses and identify associated individual and episodic event characteristics.

Qualitative: An external advisory board reviewed the clarity, breadth, and scope of the conceptual definition and item content. Modified focus groups were conducted with 20 staff and 10 family members who participated using a web-based survey. Data were extracted from Qualtrics for analysis using NVivo. Semi-structured cognitive interviews were conducted with10 health professionals working with older adults with cognitive impairment.

Quantitative: A combined exploratory and confirmatory factor analysis was performed to test for dimensionality. The explained common variance (ECV), calculated as the percent of observed variance was estimated. Estimates of internal consistency such as ordinal alpha and McDonald’s omega were computed in R and Mplus.

Data were collected from 50 staff informants on behalf of 302 residents, 25 with lucidity events. The majority (74%) of those interviewed were certified nursing assistants. Most (58%) of the sample of residents were White and 21% Black or African American. One fourth (25%) were Hispanic or Latino. Most (80% to 90%) of those with lucidity events were reported to have memory deficits and at least 70% required maximal assistance in performing basic tasks such as dressing. Most events (60%) were of short duration (10 minutes or less), and included showing facial expressions (83%) and making eye contact (88%). One half spoke multiple sentences. About half were reported to hold a conversation, and speak coherently to convey needs; 40% were able to remember and mention the name of relatives. Staff reactions were of surprise (60%) shock (52%) and happiness (50%).

Data for item modification derived from the focus groups and cognitive interviews resulted in the final lucidity measure. Internal consistency estimates were high, with most ranging from 0.76 to 0.98. The ECVs were high for most scales, indicative of essential unidimensionality.

The dimensionality and reliability analyses results were strong, and supportive of unidimensional scales with high internal consistency. The feasibility of conducting assessments of lucidity events was established.

The coronavirus disease 2019 (COVID-19) has serious physiological and psychological consequences. The long-term (>12 weeks post-infection) impact of COVID-19 on mental health, specifically in older adults, is unclear. We longitudinally assessed the association of COVID-19 with depression symptomatology in community-dwelling older adults with metabolic syndrome within the framework of the PREDIMED-Plus cohort.

Participants (n = 5486) aged 55–75 years were included in this longitudinal cohort. COVID-19 status (positive/negative) determined by tests (e.g. polymerase chain reaction severe acute respiratory syndrome coronavirus 2, IgG) was confirmed via event adjudication (410 cases). Pre- and post-COVID-19 depressive symptomatology was ascertained from annual assessments conducted using a validated 21-item Spanish Beck Depression Inventory-II (BDI-II). Multivariable linear and logistic regression models assessed the association between COVID-19 and depression symptomatology.

COVID-19 in older adults was associated with higher post-COVID-19 BDI-II scores measured at a median (interquartile range) of 29 (15–40) weeks post-infection [fully adjusted β = 0.65 points, 95% confidence interval (CI) 0.15–1.15; p = 0.011]. This association was particularly prominent in women (β = 1.38 points, 95% CI 0.44–2.33, p = 0.004). COVID-19 was associated with 62% increased odds of elevated depression risk (BDI-II ≥ 14) post-COVID-19 when adjusted for confounders (odds ratio; 95% CI 1.13–2.30, p = 0.008).

COVID-19 was associated with long-term depression risk in older adults with overweight/obesity and metabolic syndrome, particularly in women. Thus, long-term evaluations of the impact of COVID-19 on mental health and preventive public health initiatives are warranted in older adults.

To investigate the occurrence of traumatic stress symptoms (TSS) among healthcare workers active during the COVID-19 pandemic and to obtain insight as to which pandemic-related stressful experiences are associated with onset and persistence of traumatic stress.

This is a multicenter prospective cohort study. Spanish healthcare workers (N = 4,809) participated at an initial assessment (i.e., just after the first wave of the Spain COVID-19 pandemic) and at a 4-month follow-up assessment using web-based surveys. Logistic regression investigated associations of 19 pandemic-related stressful experiences across four domains (infection-related, work-related, health-related and financial) with TSS prevalence, incidence and persistence, including simulations of population attributable risk proportions (PARP).

Thirty-day TSS prevalence at T1 was 22.1%. Four-month incidence and persistence were 11.6% and 54.2%, respectively. Auxiliary nurses had highest rates of TSS prevalence (35.1%) and incidence (16.1%). All 19 pandemic-related stressful experiences under study were associated with TSS prevalence or incidence, especially experiences from the domains of health-related (PARP range 88.4–95.6%) and work-related stressful experiences (PARP range 76.8–86.5%). Nine stressful experiences were also associated with TSS persistence, of which having patient(s) in care who died from COVID-19 had the strongest association. This association remained significant after adjusting for co-occurring depression and anxiety.

TSSs among Spanish healthcare workers active during the COVID-19 pandemic are common and associated with various pandemic-related stressful experiences. Future research should investigate if these stressful experiences represent truly traumatic experiences and carry risk for the development of post-traumatic stress disorder.

Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact.

We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations.

BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI.

We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.