Despite growing healthcare coverage, disparities in access to and outcomes of psychiatric care persist, even in countries with universal healthcare. How socioeconomic status (SES), travel time, and social support individually and jointly affect psychiatric clinical trajectories remains largely unexplored.

We analyze electronic health records (EHRs) from patients diagnosed with bipolar disorder, major depressive disorder, or schizophrenia at Clínica San Juan de Dios Manizales. Using zero-inflated and standard negative binomial regression, we quantify the effects of SES, travel time, and family/social support on utilization, clinical outcomes, and symptoms of mania, psychosis, and suicidality. A mixed-effects model examines how care-seeking patterns affect visit-to-visit variability in outcomes.

Among 21,095 patients, utilization is lower for those with low SES (rate ratio [RR] 0.92, 95% CI: 0.90–0.95, p = 1.27e−10) and longer travel times (RR 0.94, 95% CI: 0.93–0.95, p = 1.19e−53). Patients with low SES are more likely to have severe symptoms (e.g., delusions: RR 1.28, 95% CI: 1.20–1.37, p = 2.57e−15) and require hospitalization (RR 1.10, 95% CI: 1.05–1.15, p = 1.94e−04), suggesting they primarily seek care when critical. Longer travel differentially affects those with low SES. However, the relationship between SES and adverse outcomes is less pronounced when living with family (e.g., hospitalizations: LRT, χ2 = 47.08, df = 3, p = 3.35e−10). Frequent outpatient care is associated with lower odds of hospitalization, suicidality, and other symptoms.

Findings demonstrate use of EHRs to model patient outcomes, the important role of social support, and need for improved healthcare accessibility.

Electronic health records (EHRs), increasingly available in low- and middle-income countries (LMICs), provide an opportunity to study transdiagnostic features of serious mental illness (SMI) and its trajectories.

Characterise transdiagnostic features and diagnostic trajectories of SMI using an EHR database in an LMIC institution.

We conducted a retrospective cohort study using EHRs from 2005–2022 at Clínica San Juan de Dios Manizales, a specialised mental health facility in Colombia, including 22 447 patients with schizophrenia (SCZ), bipolar disorder (BPD) or severe/recurrent major depressive disorder (MDD). Using diagnostic codes and clinical notes, we analysed the frequency of suicidality and psychosis across diagnoses, patterns of diagnostic switching and the accumulation of comorbidities. Mixed-effect logistic regression was used to identify factors influencing diagnostic stability.

High frequencies of suicidality and psychosis were observed across diagnoses of SCZ, BPD and MDD. Most patients (64%) received multiple diagnoses over time, including switches between primary SMI diagnoses (19%), diagnostic comorbidities (30%) or both (15%). Predictors of diagnostic switching included mentions of delusions (odds ratio = 1.47, 95% CI 1.34–1.61), prior diagnostic switching (odds ratio = 4.01, 95% CI 3.7–4.34) and time in treatment, independent of age (log of visit number; odds ratio = 0.57, 95% CI 0.54–0.61). Over 80% of patients reached diagnostic stability within 6 years of their first record.

Integrating structured and unstructured EHR data reveals transdiagnostic patterns in SMI and predictors of disease trajectories, highlighting the potential of EHR-based tools for research and precision psychiatry in LMICs.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Disturbed sleep and activity are prominent features of bipolar disorder type I (BP-I). However, the relationship of sleep and activity characteristics to brain structure and behavior in euthymic BP-I patients and their non-BP-I relatives is unknown. Additionally, underlying genetic relationships between these traits have not been investigated.

Relationships between sleep and activity phenotypes, assessed using actigraphy, with structural neuroimaging (brain) and cognitive and temperament (behavior) phenotypes were investigated in 558 euthymic individuals from multi-generational pedigrees including at least one member with BP-I. Genetic correlations between actigraphy-brain and actigraphy-behavior associations were assessed, and bivariate linkage analysis was conducted for trait pairs with evidence of shared genetic influences.

More physical activity and longer awake time were significantly associated with increased brain volumes and cortical thickness, better performance on neurocognitive measures of long-term memory and executive function, and less extreme scores on measures of temperament (impulsivity, cyclothymia). These associations did not differ between BP-I patients and their non-BP-I relatives. For nine activity-brain or activity-behavior pairs there was evidence for shared genetic influence (genetic correlations); of these pairs, a suggestive bivariate quantitative trait locus on chromosome 7 for wake duration and verbal working memory was identified.

Our findings indicate that increased physical activity and more adequate sleep are associated with increased brain size, better cognitive function and more stable temperament in BP-I patients and their non-BP-I relatives. Additionally, we found evidence for pleiotropy of several actigraphy-behavior and actigraphy-brain phenotypes, suggesting a shared genetic basis for these traits.