Ronald C. Kessler

Stein

Big data and psychiatry: advances, constraints and future directions.

Early work in psychiatry research, often involving single sites, small samples, and limited variables, has shifted to contemporary research involving multiple sites, large samples, and many variables. Such research raises important questions, including concerns about data quality and methodological rigor, uncertainty about its key lessons, issues regarding clinical relevance, and questions about how to optimize future advances. Here we consider these questions and concerns against the context of big data work on community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials. The development of large datasets allowing well-powered analyses is a major milestone, but sample size alone does not guarantee more precise estimates, and ongoing attention to the quality and rigor of big data collation and analysis is needed. Big data research has fostered trans-disciplinarity and given insights into mechanisms underlying psychiatric disorders, but also emphasizes the intricacy, heterogeneity and variability of such mechanisms, and the importance of triangulating between large-scale and small-scale research. The complexity of psychiatric phenotypes and psychobiological mechanisms contributes to the difficulty in bridging from big data to clinical application; big data research reinforces the importance of holding our diagnoses of psychiatric disorders lightly and providing explanations of these conditions humbly; and future work needs to be more attentive to clinical issues. There is enormous scope for further building databases relevant to psychiatry, but advances in conceptual models and asking the right questions are equally valuable. The full impact of big data, including artificial intelligence analyses, remains to be seen, but overenthusiastic support should be tempered by a better understanding of its strengths and limitations. At its best, such work will contribute in an iterative and integrative way to advancing our knowledge of psychiatric disorders and mental health.

Mental health impact of COVID-19 among Spanish health care workers: A 3-year longitudinal study (MINDCOVID)

AIMS: We assessed the trajectory of adverse mental health outcomes of Spanish healthcare workers (HCWs) during three years after the COVID-19 pandemic initiation, and their association with distal and proximal risk factors. METHODS: A prospective cohort of HCWs from 18 Spanish institutions was assessed seven times between May 2020 and June 2023 through institutional web-based surveys. Outcomes included: (1) any probable mental disorder (MDx)-depression (PHQ-8 ≥10), anxiety (GAD-7 ≥10), panic attack, post-traumatic stress disorder (PCL-5 ≥7), or substance use disorder (CAGE-AID ≥2); and (2) any suicidal thoughts or behaviors (STB, CSSRS). Risk factors comprised 11 distal (sociodemographic, professional, health-related) and 13 proximal (COVID-19 infection, work, health, financial, and family stressors) variables. Latent class growth analysis identified trajectories. RESULTS: Four thousand eight hundred nine were followed. Most were women (77.3%), hospital workers (57.6%), and nurses/nurse assistants (44.4%). Prevalence of any MDx declined from 45.5% (May-September 2020) to 31.2% (April-June 2023), while STB increased slightly from 8.4% to 9.4% (not significant). Common risk factors for MDx and STB were previous mental disorders, female gender, nurse assistant role, foreign-born status, primary care work, and physical comorbidities. Four trajectory classes were identified for both MDx and STB: persistent high (19.4%, 5.0%), decreasing from high prevalence (28.5%, 5.2%), increasing from low (5.9%, 4.2%), and persistent low (46.2%, 85.5%). Persistent high classes were associated with interpersonal stress, loved ones' health, lack of preparedness/protective equipment, and ethically challenging care decisions. CONCLUSIONS: Adverse mental health outcomes remained substantial among Spanish HCWs during three years of follow-up, about one in five showing persistently high burden. Continued monitoring and support are warranted.

Harmonising Depression and Anxiety Measures Across International Cohorts: A Framework Validated Using Measurement Invariance

The Relationship of Attention-Deficit/Hyperactivity Disorder With Posttraumatic Stress Disorder: A Two-Sample Mendelian Randomization and Population-Based Sibling Comparison Study

Lithium for the prevention of suicide in US veterans: a target trial emulation

BACKGROUND: Lithium has long been considered to reduce suicidal behaviour in patients with affective disorders, but evidence from large real-world populations remains limited. OBJECTIVE: To estimate the effects of lithium initiation and continuation on suicide deaths and non-lethal suicidal behaviours among adults with bipolar disorder or major depressive disorder. METHODS: We emulated two target trials using electronic health records and administrative claims from US veterans between January 2010 and December 2022. The first trial benchmarked results against the CSP-590 randomised trial by estimating the 1-year risk of suicide-related events (non-fatal suicide attempts, hospitalisations to prevent suicide or suicide deaths) among patients with a recent suicide attempt initiating lithium versus not initiating lithium. The second trial extended follow-up to 10 years to estimate risks of suicide deaths and non-lethal suicidal behaviours separately, including subgroup analyses by age and diagnosis. An additional analysis removed the requirement of a prior suicide attempt. FINDINGS: In the benchmarking analysis, the 1-year risk ratio for suicide-related events comparing lithium initiation with no initiation was 1.06 (95% CI 1.01 to 1.12), consistent with findings from the CSP-590 trial. In the extended analysis, the 10-year per-protocol risk ratio for suicide death was 1.00 (95% CI 0.86 to 1.15) and for non-lethal suicidal behaviours was 0.96 (95% CI 0.90 to 1.02). Results were similar among individuals with and without a prior suicide attempt. CONCLUSION: When added to ongoing pharmacological treatment for patients with affective disorders, lithium may not substantially reduce suicide risk highlighting the need for additional suicide prevention strategies in this population. Limitations include lack of information on adherence, dosage or blood levels of lithium which may have obscured existing differences. CLINICAL IMPLICATIONS: Our findings suggest that adding lithium therapy to the ongoing treatment regimens of patients with affective disorders for the sole purpose of reducing suicide may not be well-tolerated or meaningfully reduce suicide risk.

Mobility Patterns and Mental Health During the COVID-19 Pandemic: Longitudinal Observational Study Using Smartphone Mobility Data

Ancestry-Calibrated Polygenic Risk Scores Predict PTSD Trajectories in Recent Trauma Survivors and Interact with Neighborhood Resources

Objective: Polygenic risk scores (PRS) for posttraumatic stress disorder (PTSD) often account for a low amount of variance. Ancestry-related differences in PRS scale and variance limit cross-group comparisons. This methodological challenge further complicates gene-by-environment (GxE) analyses, given that socioenvironmental exposures are inequitably distributed across ethnoracial groups. We constructed an ancestry-calibrated polygenic risk score (AC-PRS) for PTSD in the largest longitudinal study of trauma survivors to date and investigated GxE interactions. Method: Recent trauma survivors (N=1,801) provided a blood specimen for genotyping. Six PTSD trajectories were previously identified from PTSD Checklist for DSM-5 (PCL-5) scores at 2-weeks, 8-weeks, 3-months, and 6-months post-trauma. Greenspace (normalized difference vegetation index [NDVI) and socioeconomic disadvantage (area deprivation index [ADI]) were derived from residential addresses. Logistic regressions examined interactions between newly developed AC-PRS and neighborhood factors on trajectories after adjusting for sociodemographic and trauma-related covariates. Secondary linear models considered GxE interactions on 6-month PCL-5 scores. There were no NDVI x AC-PRS interactions in any models. Conclusions: AC-PRS captures genetic risk for PTSD in admixed trauma survivors, demonstrating good discrimination between nonremitting and resilient courses of PTSD. However, neighborhood disadvantage may modify utility of PRS for PTSD, warranting careful consideration when applying these scores across contexts.

A Pilot Study of a Precision Treatment Trial Comparing Psychotherapy and Antidepressants for Depression in Indian Primary Care Settings

BackgroundDepression is commonly treated with either psychotherapy or antidepressants, but patients differ widely in their comparative responses. Identifying the most effective treatment for each individual is crucial to optimizing scarce resources. AimsTo evaluate the feasibility and acceptability of a precision treatment trial comparing psychotherapy based on behavioral activation (the Healthy Activity Program) with antidepressant medication (fluoxetine) in adults with depression in primary care settings in India. MethodsSingle-blind, two-arm randomized pilot trial in eight primary healthcare centers with adults (≥18 years) with moderate-severe depression (PHQ-9≥10). Primary outcomes assessed recruitment, retention, treatment adherence/fidelity, assessment completion, and safety procedures. Secondary outcomes included changes in depressive symptoms, remission, and patient-reported improvement at 3 months.ResultsOf 578 screened individuals, 76/180 eligible (42%) enrolled. Retention was 82% at 3 months. Psychotherapy participants attended 5.6/6-8 sessions, while medication participants consumed 57.9% of prescribed doses. Although assessment completion was high (72-100%), some measures proved burdensome, prompting refinements. Depressive symptoms improved across both arms, with 45.2% achieving remission. However, treatment responses varied substantially within each arm (psychotherapy: –9.1 [SD=6.5]; medication: –7.6 [SD=6.9]), indicating marked individual heterogeneity.ConclusionsThis pilot demonstrated the feasibility and acceptability of implementing a precision treatment trial for depression in Indian primary care and identified refinements to optimize procedures for the main trial. Both treatments improved symptoms, but the heterogeneity observed in each modality supports the potential for and importance of identifying the optimal treatment for each given patient.Trial registration: Registered at ClinicalTrials.gov (NCT06153004) on 22 November 2023.

Associations between residential segregation, ambient air pollution, and hippocampal features in recent trauma survivors

Background Residential segregation is associated with differential exposure to air pollution. Hippocampus structure and function are highly susceptible to pollutants and associated with posttraumatic stress disorder (PTSD) development. Therefore, we investigated associations between residential segregation, air pollutants, hippocampal neurobiology, and PTSD in recent trauma survivors. Methods Participants ( N = 278; 34% non-Hispanic white, 46% Non-Hispanic Black, 16% Hispanic) completed multimodal neuroimaging two weeks after trauma. Yearly averages of air pollutants (PM 2.5 and NO 2 ) and racial/economic segregation (Index of Concentration at the Extremes) were derived from each participant’s address. Linear models assessed if air pollutants mediated associations between segregation and hippocampal volume, threat reactivity, or parahippocampal cingulum fractional anisotropy (FA) after covarying for age, sex, income, and 2-week PTSD symptoms. Further models evaluated if pollutants or segregation prospectively predicted PTSD symptoms six months post-trauma. Results Non-Hispanic Black participants lived in neighborhoods with significantly greater segregation and air pollution compared to Hispanic and non-Hispanic white participants ( ps <.001). There was a significant indirect effect of NO 2 between segregation and FA values (β = 0.08, 95% CI[0.01, 0.15]), and an indirect effect of PM 2.5 between segregation and threat reactivity (β = -0.08, 95% CI[-0.14, -0.01]). There was no direct effect of segregation on hippocampal features. Pollutants and segregation were not associated with PTSD symptoms. Conclusion Residential segregation is associated with greater air pollution exposure, which is in turn associated with variability in hippocampal features among recent trauma survivors. Further research is needed to assess relationships between other environmental factors and trauma and stress-related disorders.

The relationships between sporadic and repetitive non-suicidal self-injury and mental disorders among first-year college students: results from the World Mental Health International College Student Initiative

A robust framework for harmonising health measures across international cohorts: Evidence from the COVID-19 pandemic.

SteinAnnals of Clinical Psychiatry

Early Childhood Adversity and Later Hypertension: Data from the World Mental Health Survey

Association Between Acute Plasma Glial Fibrillary Acidic Protein Levels and White Matter Integrity in Recent Trauma Survivors.

Personalised & optimised therapy (POT) algorithm using five cognitive and behavioural skills for subthreshold depression

Comprehensive mapping of cognitive and emotion networks in stress, anxiety, and depression implicates the precuneus as a critical hub

Optimizing treatment for depression in primary care using psychotherapy versus antidepressant medication in a low-resource setting: protocol for the OptimizeD randomized controlled trial

BACKGROUND: Psychotherapy and antidepressant medications are first-line treatments for depression, and they both have significant treatment effects on average. However, treatment response varies widely across patients, and neither approach is universally effective. Identifying the most effective treatment for each patient is critical everywhere, but particularly in low-resource settings where access to mental health care is limited. The Optimizing Depression (OptimizeD) trial aims to explore whether different patients respond differently to behavioral activation therapy versus antidepressant medication and if providing each patient with their optimal treatment improves outcomes in primary care. METHODS: We plan to randomize 1,500 patients with moderate to severe depression (defined as a Patient Health Questionnaire [PHQ-9] score ≥ 10) from primary healthcare settings in Bhopal, India, with equal allocation either to a culturally adapted behavioral activation therapy delivered by trained counselors (Healthy Activity Program) or to antidepressant medication (fluoxetine). Treatment will last 3 months, with remission (defined as PHQ-9 score < 5) at 3 months as the primary endpoint. Using machine learning, we will attempt to develop a precision treatment rule that leverages baseline clinical, psychological, cognitive, socioeconomic, and biological data to predict which treatment is most likely to achieve remission for each patient. Cost-effectiveness analysis will then assess whether the added costs of optimizing treatment are justified by improvements in remission, recovery, and cost savings at the health system and societal levels. Secondary and exploratory objectives include assessing the effectiveness of optimization in a range of secondary outcomes, evaluating treatment mechanisms, and exploring whether incorporating genetic and biological markers as predictors improves treatment optimization. DISCUSSION: The OptimizeD trial will evaluate whether baseline information collected in routine care can inform optimal depression treatment selection and identify predictors of nonresponse to facilitate timely specialist referrals. Findings have the potential to enhance personalized depression care in primary health systems, particularly in low-resource settings, with broader implications for global public health. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05944926; registered July 2, 2023) and Clinical Trials Registry India (CTRI/2024/01/061932; registered January 29, 2024).

Predicting Traumatic Brain Injury Post-Trauma Using Temporal Attention on Sleep-Wake Data

BACKGROUND: Traumatic Brain Injury (TBI) is a major public health concern, and accurate classification is essential for effective treatment and improved patient outcomes. Sleep/wake behavior has emerged as a potential biomarker for TBI classification, yet the optimal time window in which to identify sleep/wake changes after TBI remains unclear. METHODS: We evaluated daily longitudinal sleep/wake data from a prospective cohort of more than 2,000 emergency department patients with and without blood biomarker-documented TBI (Glial Fibrillary Acidic Protein - GFAP > 268 pg/ml). We utilized a deep learning model to identify the impact of time from trauma and duration of data collection on the model's ability to distinguish between TBI-positive (TBI+) and TBI-negative (TBI-) cases. RESULTS: Our analysis showed that sleep/wake data from the first 7 days after TBI most accurately identified TBI. Sleep-wake data from the first 7, 14, and 21 days after trauma achieved sensitivity/specificity of 81%/25%, 40%/66%, and 45%/58%, respectively. F1 scores of deep learning models developed from the first 7, 14, and 21 days were 22%, 21%, and 20%, respectively. CONCLUSIONS: The results suggest that early sleep/wake data has promise for assisting with TBI identification. SIGNIFICANCE: In the future, the incorporation of sleep/wake derived biomarkers into TBI identification tools could assist in the identification of individuals with potential TBI for further screening and intervention.

Development of a Baseline Battery to Optimize Depression Treatment Assignment: Balancing Breadth and Brevity

Comorbidity and temporal associations between mental disorders among college students in the world mental health international college student initiative

BACKGROUND: Mental disorders are highly prevalent among students worldwide. This study aims to examine comorbidity and temporal associations between mental disorders among students. METHODS: The study included 72,288 students from 18 countries as part of the World Mental Health International College Student (WMH-ICS) Initiative, with cross-sectional data collected between 2017 and 2023. Screening for common DSM-5 disorders was conducted using validated screening measures. Latent variables were examined using exploratory principal axis factor analysis on a correlation matrix among the lifetime mental disorders. Based on age-of-onset information, multivariable poisson regression models were used to examine associations of prior disorders with the first onset of other disorders. RESULTS: 27.0 % of students screened positive for only one lifetime disorder, 17.1 % for two, 10.9 % for three, and 10.6 % for 4+ disorders. In the factor analysis, three latent variables were found, comprising: internalizing disorders (generalized anxiety disorder, major depressive episode, post-traumatic stress disorder, and panic disorder), substance use disorders (drug use disorder and alcohol use disorder), and externalizing disorders (attention deficit/hyperactivity disorder and mania/hypomania). Prior internalizing and externalizing disorders were associated with the subsequent first onset of all other disorders with risk ratios ranging from 1.5-7.5. Substance use disorders were less consistently associated with the subsequent first onset of other disorders, but alcohol use disorder was associated with the first onset of drug use disorder and vice versa. CONCLUSIONS: Mental disorder comorbidity is common among students, and students with disorders across the internalizing and externalizing spectrum have an increased risk of future mental disorder comorbidities.

Early life adversity increases risk for chronic post-traumatic pain, data from humans and rodents

Traumatic stress exposures (TSEs) are common in life. Although most individuals recover after a TSE, a substantial subset develop adverse post-traumatic neuropsychiatric sequelae such as chronic post-traumatic musculoskeletal pain (CPMP). Vulnerability factors for CPMP are poorly understood, which hinders identification of high-risk individuals for targeted interventions. One known vulnerability factor for many pain types is exposure to early life adversity (ELA), but few studies have assessed whether ELA increases risk for CPMP. This study used data from the Advancing Understanding of RecOvery afteR traumA study, a prospective human cohort study of TSE survivors, to test the hypothesis that ELA increases risk for CPMP. In addition, in secondary analyses, we assessed which subtypes of ELA (including childhood bullying) were most predictive of CPMP and whether a rat ELA model consisting of neonatal limited bedding, combined with single prolonged stress (SPS) in adulthood, would accurately model human findings. In Advancing Understanding of RecOvery afteR traumA study participants (n = 2480), using multinomial logistic regression modeling of 4 identified latent pain classes, we found that ELA increased vulnerability to the high unremitting pain class (odds ratio [OR] = 1.047, P < 0.001), the moderate pain class (OR = 1.031, P < 0.001), and the moderate recovery pain class (OR = 1.018, P = 0.004), with physical abuse, emotional abuse, and bullying being the strongest predictors of high pain class assignment. Similarly, in male and female Sprague Dawley rats, in comparison with SPS alone, neonatal limited bedding combined with SPS caused increased baseline sensitivity and prolonged mechanical hypersensitivity (F(11,197) = 3.22, P < 0.001). Further studies in animals and humans are needed to understand mechanisms by which ELA confers vulnerability to CPMP.