Acute stroke treatments are highly time-sensitive, with geographical disparities affecting access to care. This study examined the impact of driving distance to the nearest comprehensive stroke center (CSC) and rurality on the use of thrombectomy or thrombolysis in Ontario, Canada.

This retrospective cohort study used administrative data to identify adults hospitalized with acute ischemic stroke between 2017 and 2022. Driving time from patients’ residences to the nearest CSC was calculated using the Ontario Road Network File and postal codes. Rurality was categorized using postal codes. Multivariable logistic regression, adjusted for baseline differences, estimated the association between driving distance and treatment with thrombectomy (primary outcome) or thrombolysis (secondary outcome). Driving time was modeled as a continuous variable using restricted cubic splines.

Data from 57,678 patients (median age 74 years, IQR 64–83) were analyzed. Increased driving time was negatively associated with thrombectomy in a nonlinear fashion. Patients living 120 minutes from a CSC were 20% less likely to receive thrombectomy (adjusted odds ratio [aOR] 0.80, 95% CI 0.62–1.04), and those 240 minutes away were 60% less likely (aOR 0.41, 95% CI 0.28–0.60). Driving time did not affect thrombolysis rates, even at 240 minutes (aOR 1.0, 95% CI 0.70–1.42). Thrombectomy use was similar in medium urban areas (aOR 0.80, 95% CI 0.56–1.16) and small towns (aOR 0.78, 95% CI 0.57–1.06) compared to large urban areas.

Thrombolysis access is equitable across Ontario, but thrombectomy access decreases with increased driving distance to CSCs. A multifaceted approach, combining healthcare policy innovation and infrastructure development, is necessary for equitable thrombectomy delivery.

Individuals with diminished social connections are at higher risk of mental disorders, dementia, circulatory conditions and musculoskeletal conditions. However, evidence is limited by a disease-specific focus and no systematic examination of sex differences or the role of pre-existing mental disorders.

We conducted a cohort study using data on social disconnectedness (loneliness, social isolation, low social support and a composite measure) from the 2013 and 2017 Danish National Health Survey linked with register data on 11 broad categories of medical conditions through 2021. Poisson regression was applied to estimate incidence rate ratios (IRRs), incidence rate differences (IRDs), and explore sex differences and interaction with pre-existing mental disorders.

Among 162,497 survey participants, 7.6%, 3.5% and 14.8% were classified as lonely, socially isolated and with low social support, respectively. Individuals who were lonely and with low social support had a higher incidence rate in all 11 categories of medical conditions (interquartile range [IQR] of IRRs, respectively 1.26–1.49 and 1.10–1.14), whereas this was the case in nine categories among individuals who were socially isolated (IQR of IRRs, 1.01–1.31). Applying the composite measure, the highest IRR was 2.63 for a mental disorder (95% confidence interval [CI], 2.38–2.91), corresponding to an IRD of 54 (95% CI, 47–61) cases per 10,000 person-years. We found sex and age differences in some relative and absolute estimates, but no substantial deviations from additive interaction with pre-existing mental disorders.

This study advances our knowledge of the risk of medical conditions faced by individuals who are socially disconnected. In addition to the existing evidence, we found higher incidence rates for a broad range of medical condition categories. Contrary to previous evidence, our findings suggest that loneliness is a stronger determinant for subsequent medical conditions than social isolation and low social support.

A preregistered analysis plan and statistical code are available at Open Science Framework (https://osf.io/pycrq).

Type 2 diabetes (T2D) is a global health burden, more prevalent among individuals with attention deficit hyperactivity disorder (ADHD) compared to the general population. To extend the knowledge base on how ADHD links to T2D, this study aimed to estimate causal effects of ADHD on T2D and to explore mediating pathways.

We applied a two-step, two-sample Mendelian randomization (MR) design, using single nucleotide polymorphisms to genetically predict ADHD and a range of potential mediators. First, a wide range of univariable MR methods was used to investigate associations between genetically predicted ADHD and T2D, and between ADHD and the purported mediators: body mass index (BMI), childhood obesity, childhood BMI, sedentary behaviour (daily hours of TV watching), blood pressure (systolic blood pressure, diastolic blood pressure), C-reactive protein and educational attainment (EA). A mixture-of-experts method was then applied to select the MR method most likely to return a reliable estimate. We used estimates derived from multivariable MR to estimate indirect effects of ADHD on T2D through mediators.

Genetically predicted ADHD liability associated with 10% higher odds of T2D (OR: 1.10; 95% CI: 1.02, 1.18). From nine purported mediators studied, three showed significant individual mediation effects: EA (39.44% mediation; 95% CI: 29.00%, 49.73%), BMI (44.23% mediation; 95% CI: 34.34%, 52.03%) and TV watching (44.10% mediation; 95% CI: 30.76%, 57.80%). The combination of BMI and EA explained the largest mediating effect (53.31%, 95% CI: −1.99%, 110.38%) of the ADHD–T2D association.

These findings suggest a potentially causal, positive relationship between ADHD liability and T2D, with mediation through higher BMI, more TV watching and lower EA. Intervention on these factors may thus have beneficial effects on T2D risk in individuals with ADHD.

Adolescence is a period marked by highest vulnerability to the onset of depression, with profound implications for adult health. Neuroimaging studies have revealed considerable atrophy in brain structure in these patients with depression. Of particular importance are regions responsible for cognitive control, reward, and self-referential processing. However, the causal structural networks underpinning brain region atrophies in adolescents with depression remain unclear.

This study aimed to investigate the temporal course and causal relationships of gray matter atrophy within the brains of adolescents with depression.

We analyzed T1-weighted structural images using voxel-based morphometry in first-episode adolescent patients with depression (n=80, 22 males; age = 15.57±1.78) and age, gender matched healthy controls (n=82, 25 males; age = 16.11±2.76) to identify the disease stage-specific gray matter abnormalities. Then, with granger causality analysis, we arranged the patients’ illness duration chronologically to construct the causal structural covariance networks that investigated the causal relationships of those atypical structures.

Compared to controls, smaller volumes in ventral medial prefrontal cortex (vmPFC), dorsal anterior cingulate cortex (dACC), middle cingulate cortex (MCC) and insula areas were identified in patients with less than 1 year illness duration, and further progressed to the subgenual ACC, regions of default, frontoparietal networks in longer duration. Causal network results revealed that dACC, vmPFC, MCC and insula were prominent nodes projecting exerted positive causal effects to regions of the default mode and frontoparietal networks. The dACC, vmPFC and insula also had positive projections to the reward network, which included mainly the thalamus, caudate and putamen, while MCC also exerted a positive causal effect on the insula and thalamus.

These findings revealed the progression of structural atrophy in adolescent patients with depression and demonstrated the causal relationships between regions involving cognitive control, reward and self-referential processes.

None Declared

The concept of Mental Health Literacy (MHL) is inherently multidimensional. However, the interrelationships among its various dimensions remain insufficiently elucidated. In recent years, the textual analysis of social media posts has emerged as a promising methodological approach for longitudinal research in this domain.

This study aimed to investigate whether temporal causal associations exist between recognition of mental illness (R), mental illness stigma (S), help-seeking efficacy (HE), maintenance of positive mental health (M), and help-seeking attitude (HA).

Tweets were collocted at three distinct time points: T1, T2, and T3, spanning the period from November 1, 2021, to December 31, 2022. We employed a machine-learning approach to categorize the posts into five MHL facets. Using these facets, we trained a machine learning model, specifically Bidirectional Encoder Representations from Transformers (BERT), to determine the MHL scores. To be eligible, an account must have an R facet score at T1, and M, S, HE facet scores at T2, as well as an HA facet score at T3. In total, we retrieved 4,471,951 MHL-related tweets from 941 users. We further employed structural equation modeling to validate the causal relationships within the MHL framework.

In the evaluation, BERT achieved average accuracy scores exceeding 89% across the five MHL facets in the validation set, along with F1-scores ranging between 0.75 and 0.89. Among the five MHL facets—maintenance of positive mental health, recognition of mental illness, help-seeking efficacy, and help-seeking attitudes—each demonstrated a statistically significant positive correlation with the others. Conversely, mental illness stigma exhibited a statistically significant negative correlation with the remaining four facets. In the analysis using single-mediation models, each of the individual mediator variables—namely, mental illness stigma, help-seeking efficacy, and maintenance of positive mental health—exhibited significant indirect effects. In the multiple-mediation model, two mediator variables—help-seeking efficacy and maintenance of positive mental health—demonstrated significant indirect effects. These findings suggested that the recognition of mental illness exerted an influence on help-seeking attitudes through one or more of these mediators.

By leveraging machine learning techniques for the textual analysis of social media and employing a longitudinal research design with panel data, this study elucidates the potential mechanisms through which the MHL framework influences attitudes toward seeking mental health services. These insights hold significant implications for the design of future interventions and the development of targeted policies aimed at promoting help-seeking behaviors.

None Declared

Globally, there is a mental health crisis, and anxiety is the most prevalent mental health condition. However, the impact of the COVID-19 pandemic (COVID) on generalized anxiety disorder (GAD) prevalence has not been quantified across European countries, and such impact could establish a new baseline of GAD estimates in European countries.

To assess GAD by severity level before and during COVID in 5 European countries, using the 7-Item GAD Questionnaire (GAD-7).

Adults (age 18+) in France, Germany, UK, Italy, and Spain completed a short survey in May 2020 to assess the impact of COVID on their mental health. All respondents had previously participated in the National Health and Wellness Survey, a nationally representative survey of the adult general population in each country, before COVID (December 2019–March 2020). In both surveys, respondents completed the GAD-7. GAD symptoms were defined by GAD-7 score as mild (5-9), moderate (10-14), and severe GAD (≥15). Positive screen was defined as GAD-7 score ≥10. Positive screen and GAD symptom severity prevalence were reported for the pooled European sample and by country, both before and during COVID. Chi-square and McNemar’s tests were used to evaluate the difference in GAD severity across countries and changes over baseline in GAD positive screen during COVID. P-values were reported for both tests.

In total, 2401 adults were included in analysis (France, n=482; Germany, n=487; UK, n=487; Italy, n=474; Spain, n=471). Prior to COVID, 311 (13%) screened positive for GAD, with 208 (9%) moderate and 103 (4%) severe in the pooled European sample. During COVID, the distribution of GAD symptoms almost doubled, as 576 (24%) screened positive for GAD, and shifted towards greater severity with 337 (14%) moderate and 239 (10%) severe in the pooled European sample (Figure 1). Before COVID, the prevalence of positive screen ranged from 11% (France, Germany, Spain) to 16% (UK). Statistically significant increases in positive screen over baseline levels were observed across all countries (p<0.01), except Germany. Spain was the most impacted by COVID (increase: 16%), followed by Italy, France, and UK (increase: 14%, 12%, and 9%, respectively). Germany was the least affected, overall (increase: 4%) (Figure 2).

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Image 2:

During COVID, estimates of positive screen for GAD increased substantially to 24% across 5 European countries. Surges in positive screen and GAD symptom severity were observed in all 5 countries, with more profound impact in Spain, Italy, France, and UK. With new baseline GAD estimates, the country-specific data of COVID impact on GAD could help to inform appropriate allocation of mental health resources.

D. Karlin Employee of: MindMed, S. Suponcic Shareolder of: Eli Lilly, Stryker, Abbott, Amgen, Consultant of: MindMed, Becton Dickinson Company, CSL Behring, N. Chen Consultant of: MindMed, C. Steinhart Employee of: MindMed, P. Duong Employee of: MindMed

Digital interventions have been found to be successful in preventing occupational mental health concerns, however, they seem to be affected by attrition bias through high attrition rates and differential attrition. Differential attrition arises when the rates of participant dropouts differ across different treatment conditions and is considered a significant challenge to internal validity.

We aimed at systematically review and meta-analyse differential attrition of digital mental health interventions in the workplace setting.

On January 2, 2022, we performed a search in the following electronic databases: PubMed, Scopus, and Web of Science Core. We utilized a combination of terms from five distinct areas, namely mental health, intervention, workplace, implementation, and study design. The study encompassed adult employees who took part in a randomized control trial aimed at preventing mental health issues in the workplace through an online intervention. A team of six reviewers collaborated on the study selection process, while two independent researchers conducted the data extraction for the selected studies. We performed a meta-analysis of the log-transformed relative attrition rates of the included studies using a random-effects model with limited maximum-likelihood (REML) estimation to account for the degree of heterogeneity.

A total of 19 studies were included in the meta-analysis. For baseline to post-intervention, the average total attrition was 26.27% (SD = 21.16%, range = 0 – 66.3%) and the random effects model revealed a higher attrition rate in the intervention group compared to the control group, with a pooled risk ratio of 1.05 (95% CI: 1.01 - 1.10, p = .014). For baseline to follow-up measurement the average total attrition was 27.71% (SD = 20.80%, range = 0 – 67.78%), however, in this case the random effects model did not indicate a higher attrition in the intervention group when compared to the control group (pooled risk ratio = 1.05, 95% CI: 0.98 – 1.12, p = .183).

There is an indication of higher attrition in the intervention group as compared to the control group in occupational e-mental health interventions from baseline to post-intervention, however this does not seem to be the case for baseline to follow-up attrition. These results should be taken into account in the design process of studies and statistical analyses should be adapted to counteract the bias that could result from differential attrition.

None Declared