Mental disorders affect nearly 970 million people worldwide, impacting individuals and healthcare systems. Large population databases offer insights often unattainable in smaller studies, but their findings may not always generalize across diverse regions. To address this, we introduce a European cohort from Catalonia, Spain, allowing for comparisons between individuals with mental disorders and the general population.

Data were obtained from the “Programa d’analítica de dades per a la recerca i la innovació en salut” (PADRIS). The cohort included all individuals who accessed public specialized mental health services between 2015 and 2019, with retrospective follow-up extending to 2010. These individuals, referred to as cases, were matched by age, sex, and health region with controls, individuals who had no interactions with mental health services during the same period. Sociodemographic and clinical characteristics, including psychiatric diagnoses, comorbidities, smoking status, healthcare utilization, and prescribed treatments, were analyzed.

The study included 1,421,510 individuals (mean age: 41.6±22.1; 53.6% female), with 473,812 cases and 947,698 controls. Cases were more likely to be exempt from income reporting, be ever-smokers, and have musculoskeletal comorbidities. A total of 1,547,374 psychiatric diagnoses were recorded, with anxiety (31.38%) and mood disorders (18.83%) being the most frequent. Over the follow-up, 76.2 million primary care visits and 67.1 million prescriptions were recorded.

This cohort enhances our understanding of mental health service use, diagnostic trends, and treatment patterns in Catalonia. The insights derived from this cohort have the potential to inform mental health policies, improving outcomes within and beyond the region.

The prevalence of mental health disorders has significantly increased in recent years, posing substantial challenges to healthcare systems worldwide, particularly primary care (PC) settings. This study examines trends in mental health diagnoses in PC settings in Catalonia from 2010 to 2019 and identifies associated sociodemographic, clinical characteristics, psychopharmacological treatments, and resource utilization patterns.

Data from 947,698 individuals without prior severe mental illness, derived from the Data Analytics Program for Health Research and Innovation (PADRIS), were analyzed for this study. Sociodemographic data, diagnoses, and resource utilization were extracted from electronic health records. Descriptive statistics, chi-square tests, Mann–Whitney tests, and a multivariate binary logistic regression were employed to analyze the data.

Over the study period, 172,112 individuals (18.2%) received at least one mental health diagnosis in PC, with unspecified anxiety disorder (40.5%), insomnia (15.7%) and unspecified depressive disorder (10.2%) being the most prevalent. The prevalence of these diagnoses increased steadily until 2015 and stabilized thereafter. Significant associations were found between mental health diagnoses, female sex, lower socioeconomic status, higher BMI, and smoking status in a multivariate binary logistic regression.

This study highlights a growing burden of stress-related mental health diagnoses in PC in Catalonia, driven by demographic and socioeconomic factors. These findings may be indicative of broader trends across Europe and globally. Addressing this rising prevalence requires innovative approaches and collaborative strategies that extend beyond traditional healthcare resources. Engaging stakeholders is essential for implementing effective, sustainable solutions that promote mental health in Catalonia and potentially inform similar initiatives worldwide.

Bipolar disorder is highly prevalent and consists of biphasic recurrent mood episodes of mania and depression, which translate into altered mood, sleep and activity alongside their physiological expressions.

The IdenTifying dIgital bioMarkers of illnEss activity and treatment response in BipolAr diSordEr with a novel wearable device (TIMEBASE) project aims to identify digital biomarkers of illness activity and treatment response in bipolar disorder.

We designed a longitudinal observational study including 84 individuals. Group A comprises people with acute episode of mania (n = 12), depression (n = 12 with bipolar disorder and n = 12 with major depressive disorder (MDD)) and bipolar disorder with mixed features (n = 12). Physiological data will be recorded during 48 h with a research-grade wearable (Empatica E4) across four consecutive time points (acute, response, remission and episode recovery). Group B comprises 12 people with euthymic bipolar disorder and 12 with MDD, and group C comprises 12 healthy controls who will be recorded cross-sectionally. Psychopathological symptoms, disease severity, functioning and physical activity will be assessed with standardised psychometric scales. Physiological data will include acceleration, temperature, blood volume pulse, heart rate and electrodermal activity. Machine learning models will be developed to link physiological data to illness activity and treatment response. Generalisation performance will be tested in data from unseen patients.

Recruitment is ongoing.

This project should contribute to understanding the pathophysiology of affective disorders. The potential digital biomarkers of illness activity and treatment response in bipolar disorder could be implemented in a real-world clinical setting for clinical monitoring and identification of prodromal symptoms. This would allow early intervention and prevention of affective relapses, as well as personalisation of treatment.