Determining Distinct Suicide Attempts From Recurrent Electronic Health Record Codes: Classification Study.

BACKGROUND: Prior suicide attempts are a relatively strong risk factor for future suicide attempts. There is growing interest in using longitudinal electronic health record (EHR) data to derive statistical risk prediction models for future suicide attempts and other suicidal behavior outcomes. However, model performance may be inflated by a largely unrecognized form of "data leakage" during model training: diagnostic codes for suicide attempt outcomes may refer to prior attempts that are also included in the model as predictors. OBJECTIVE: We aimed to develop an automated rule for determining when documented suicide attempt diagnostic codes identify distinct suicide attempt events. METHODS: From a large health care system's EHR, we randomly sampled suicide attempt codes for 300 patients with at least one pair of suicide attempt codes documented at least one but no more than 90 days apart. Supervised chart reviewers assigned the clinical settings (ie, emergency department [ED] versus non-ED), methods of suicide attempt, and intercode interval (number of days). The probability (or positive predictive value) that the second suicide attempt code in a given pair of codes referred to a distinct suicide attempt event from its preceding suicide attempt code was calculated by clinical setting, method, and intercode interval. RESULTS: Of 1015 code pairs reviewed, 835 (82.3%) were nonindependent (ie, the 2 codes referred to the same suicide attempt event). When the second code in a pair was documented in a clinical setting other than the ED, it represented a distinct suicide attempt 3.3% of the time. The more time elapsed between codes, the more likely the second code in a pair referred to a distinct suicide attempt event from its preceding code. Code pairs in which the second suicide attempt code was assigned in an ED at least 5 days after its preceding suicide attempt code had a positive predictive value of 0.90. CONCLUSIONS: EHR-based suicide risk prediction models that include International Classification of Diseases codes for prior suicide attempts as a predictor may be highly susceptible to bias due to data leakage in model training. We derived a simple rule to distinguish codes that reflect new, independent suicide attempts: suicide attempt codes documented in an ED setting at least 5 days after a preceding suicide attempt code can be confidently treated as new events in EHR-based suicide risk prediction models. This rule has the potential to minimize upward bias in model performance when prior suicide attempts are included as predictors in EHR-based suicide risk prediction models.

The molecular genetic landscape of human brain size variation.

Human brain size changes dynamically through early development, peaks in adolescence, and varies up to 2-fold among adults. However, the molecular genetic underpinnings of interindividual variation in brain size remain unknown. Here, we leveraged postmortem brain RNA sequencing and measurements of brain weight (BW) in 2,531 individuals across three independent datasets to identify 928 genome-wide significant associations with BW. Genes associated with higher or lower BW showed distinct neurodevelopmental trajectories and spatial patterns that mapped onto functional and cellular axes of brain organization. Expression of BW genes was predictive of interspecies differences in brain size, and bioinformatic annotation revealed enrichment for neurogenesis and cell-cell communication. Genome-wide, transcriptome-wide, and phenome-wide association analyses linked BW gene sets to neuroimaging measurements of brain size and brain-related clinical traits. Cumulatively, these results represent a major step toward delineating the molecular pathways underlying human brain size variation in health and disease.

Characterizing the phenotypic and genetic structure of psychopathology in UK Biobank.

Mental conditions exhibit a higher-order transdiagnostic factor structure which helps to explain the widespread comorbidity observed in psychopathology. However, the phenotypic and genetic structures of psychopathology may differ, raising questions about the validity and utility of these factors. Here, we study the phenotypic and genetic factor structures of ten psychiatric conditions using UK Biobank and public genomic data. Although the factor structure of psychopathology was generally genetically and phenotypically consistent, conditions related to externalizing (e.g., alcohol use disorder) and compulsivity (e.g., eating disorders) exhibited cross-level disparities in their relationships with other conditions, plausibly due to environmental influences. Domain-level factors, especially thought disorder and internalizing factors, were more informative than a general psychopathology factor in genome-wide association and polygenic index analyses. Collectively, our findings enhance the understanding of comorbidity and shared etiology, highlight the intricate interplay between genes and environment, and offer guidance for psychiatric research using polygenic indices.

Trajectories of common mental disorders symptoms before and during the COVID-19 pandemic: findings from the ELSA-Brasil COVID-19 Mental Health Cohort.

AIM: Evidence indicates most people were resilient to the impact of the COVID-19 pandemic on mental health. However, evidence also suggests the pandemic effect on mental health may be heterogeneous. Therefore, we aimed to identify groups of trajectories of common mental disorders' (CMD) symptoms assessed before (2017-19) and during the COVID-19 pandemic (2020-2021), and to investigate predictors of trajectories. METHODS: We assessed 2,705 participants of the ELSA-Brasil COVID-19 Mental Health Cohort study who reported Clinical Interview Scheduled-Revised (CIS-R) data in 2017-19 and Depression Anxiety Stress Scale-21 (DASS-21) data in May-July 2020, July-September 2020, October-December 2020, and April-June 2021. We used an equi-percentile approach to link the CIS-R total score in 2017-19 with the DASS-21 total score. Group-based trajectory modeling was used to identify CMD trajectories and adjusted multinomial logistic regression was used to investigate predictors of trajectories. RESULTS: Six groups of CMD symptoms trajectories were identified: low symptoms (17.6%), low-decreasing symptoms (13.7%), low-increasing symptoms (23.9%), moderate-decreasing symptoms (16.8%), low-increasing symptoms (23.3%), severe-decreasing symptoms (4.7%). The severe-decreasing trajectory was characterized by age < 60 years, female sex, low family income, sedentary behavior, previous mental disorders, and the experience of adverse events in life. LIMITATIONS: Pre-pandemic characteristics were associated with lack of response to assessments. Our occupational cohort sample is not representative. CONCLUSION: More than half of the sample presented low levels of CMD symptoms. Predictors of trajectories could be used to detect individuals at-risk for presenting CMD symptoms in the context of global adverse events.

Association of Everyday Discrimination With Depressive Symptoms and Suicidal Ideation During the COVID-19 Pandemic in the All of Us Research Program.

Importance: The COVID-19 pandemic has coincided with an increase in depressive symptoms as well as a growing awareness of health inequities and structural racism in the United States. Objective: To examine the association of mental health with everyday discrimination during the pandemic in a large and diverse cohort of the All of Us Research Program. Design, Setting, and Participants: Using repeated assessments in the early months of the pandemic, mixed-effects models were fitted to assess the associations of discrimination with depressive symptoms and suicidal ideation, and inverse probability weights were applied to account for nonrandom probabilities of completing the voluntary survey. Main Outcomes and Measures: The exposure and outcome measures were ascertained using the Everyday Discrimination Scale and the 9-item Patient Health Questionnaire (PHQ-9), respectively. Scores for PHQ-9 that were greater than or equal to 10 were classified as moderate to severe depressive symptoms, and any positive response to the ninth item of the PHQ-9 scale was considered as presenting suicidal ideation. Results: A total of 62 651 individuals (mean [SD] age, 59.3 [15.9] years; female sex at birth, 41 084 [65.6%]) completed at least 1 assessment between May and July 2020. An association with significantly increased likelihood of moderate to severe depressive symptoms and suicidal ideation was observed as the levels of discrimination increased. There was a dose-response association, with 17.68-fold (95% CI, 13.49-23.17; P < .001) and 10.76-fold (95% CI, 7.82-14.80; P < .001) increases in the odds of moderate to severe depressive symptoms and suicidal ideation, respectively, on experiencing discrimination more than once a week. In addition, the association with depressive symptoms was greater when the main reason for discrimination was race, ancestry, or national origins among Hispanic or Latino participants at all 3 time points and among non-Hispanic Asian participants in May and June 2020. Furthermore, high levels of discrimination were as strongly associated with moderate to severe depressive symptoms as was history of prepandemic mood disorder diagnosis. Conclusions and Relevance: In this large and diverse sample, increased levels of discrimination were associated with higher odds of experiencing moderate to severe depressive symptoms. This association was particularly evident when the main reason for discrimination was race, ancestry, or national origins among Hispanic or Latino participants and, early in the pandemic, among non-Hispanic Asian participants.

Effects of social support on depression risk during the COVID-19 pandemic: What support types and for whom?

Background: Rates of depression have increased worldwide during the COVID-19 pandemic. One known protective factor for depression is social support, but more work is needed to quantify the extent to which social support could reduce depression risk during a global crisis, and specifically to identify which types of support are most helpful, and who might benefit most. Methods: Data were obtained from participants in the All of Us Research Program who responded to the COVID-19 Participant Experience (COPE) survey administered monthly from May 2020 to July 2020 (N=69,066, 66% female). Social support was assessed using 10 items measuring emotional/informational support (e.g., someone to confide in or talk to about yourself or your problems), positive social interaction support (e.g., someone to do things with to help you get your mind off things), and tangible support (e.g., someone to help with daily chores if sick). Elevated depression symptoms were defined based on having a moderate-to-severe (≥10) score on the Patient Health Questionnaire (PHQ-9). Mixed-effects logistic regression models were used to test associations across time between overall social support and its subtypes with depression, adjusting for age, sex, race, ethnicity, and socioeconomic factors. We then assessed interactions between social support and potential effect modifiers: age, sex, pre-pandemic mood disorder, and pandemic-related stressors (e.g., financial insecurity). Results: Approximately 16% of the sample experienced elevated depressive symptoms. Overall social support was associated with significantly reduced odds of depression (adjusted odds ratio, aOR [95% CI]=0.44 [0.42-0.45]). Among subtypes, emotional/informational support (aOR=0.42 [0.41-0.43]) and positive social interactions (aOR=0.43 [0.41-0.44]) showed the largest protective associations with depression, followed by tangible support (aOR=0.63 [0.61-0.65]). Sex, age, and pandemic-related financial stressors were statistically significant modifiers of the association between social support and depression. Conclusions: Individuals reporting higher levels of social support were at reduced risk of depression during the early COVID-19 pandemic. The perceived availability of emotional support and positive social interactions, more so than tangible support, was key. Individuals more vulnerable to depression (e.g., women, younger individuals, and those experiencing financial stressors) may particularly benefit from enhanced social support, supporting a precision prevention approach.

Community-Level Social Support Infrastructure and Adult Onset of Major Depressive Disorder in a South Asian Postconflict Setting.

IMPORTANCE: Individual-level social support protects against major depressive disorder (MDD) among adults exposed to trauma. Little is known about the consequences of community-level interventions in the general population. OBJECTIVE: To determine the potential consequences of neighborhood social infrastructure on incident MDD in a high-risk general population. DESIGN, SETTING, AND PARTICIPANTS: This longitudinal, multilevel study estimated associations between a neighborhood-level program in a case-control design and subsequent individual outcomes across 10 years (2006-2015) in a cohort of young adults. Exogenously placed social programs simulate natural experiment conditions in a high-poverty population experiencing armed conflict (1998-2006). The western Chitwan valley in Nepal has a general population at high risk of MDD, with neighborhoods exposed to interventions to improve social support. From a random sample (response rate 93%) selected to represent the general population in 2016, participants aged 25 to 34 years in 2006 were studied. These individuals resided within 149 neighborhoods that varied in their availability of active social support programs. The analyses were conducted between October 2020 and November 2021. EXPOSURES: The Small Farmers Development Program (SFDP) uses shared, joint liability financial credit among neighbors to build social capital and cohesion within neighborhoods. MAIN OUTCOMES AND MEASURES: Onset of DSM-IV MDD after the conflict, assessed by the Nepal-specific, clinically validated World Mental Health Composite International Diagnostic Interview with a life history calendar. The hypothesis tested was that exposure to SFDP reduced adult onset of MDD. RESULTS: Of the 1917 survey participants, 886 (46.2%) were women, and 856 (44.7%) were of Brahmin or Chhetri ethnicity. Of the 149 neighborhoods, 21 had an active SFDP group, and 156 of 1917 (8.1%) participants experienced MDD between 2006 and 2015. Discrete-time hazard models showed participants living in neighborhoods with an SFDP experienced incident MDD at nearly half the rate as others (odds ratio = 0.55; 95% CI, 0.30-1.02; P = .06). A multivariate, multilevel matching analysis showed the incidence of MDD among adults living in neighborhoods with an SFDP was 19 of 256 (7.4%), compared with 33 of 256 (12.9%) in the matched sample with no SFDP (z = 2.05; P = .04). CONCLUSIONS AND RELEVANCE: Living in a neighborhood with community-level social support infrastructure was associated with reduced subsequent rates of adult-onset MDD, even in this high-risk population. Investments in such infrastructure may reduce population-level MDD, supporting clinical focus on potentially unpreventable cases.

Use of the PsycheMERGE Network to Investigate the Association Between Depression Polygenic Scores and White Blood Cell Count.

Importance: Although depression is a common psychiatric disorder, its underlying biological basis remains poorly understood. Pairing depression polygenic scores with the results of clinical laboratory tests can reveal biological processes involved in depression etiology and in the physiological changes resulting from depression. Objective: To characterize the association between depression polygenic scores and an inflammatory biomarker, ie, white blood cell count. Design, Setting, and Participants: This genetic association study was conducted from May 19, 2019, to June 5, 2021, using electronic health record data from 382 452 patients across 4 health care systems. Analyses were conducted separately in each health care system and meta-analyzed across all systems. Primary analyses were conducted in Vanderbilt University Medical Center's biobank. Replication analyses were conducted across 3 other PsycheMERGE sites: Icahn School of Medicine at Mount Sinai, Mass General Brigham, and the Million Veteran Program. All patients with available genetic data and recorded white blood cell count measurements were included in the analyses. Primary analyses were conducted in individuals of European descent and then repeated in a population of individuals of African descent. Exposures: Depression polygenic scores. Main Outcomes and Measures: White blood cell count. Results: Across the 4 PsycheMERGE sites, there were 382 452 total participants of European ancestry (18.7% female; median age, 57.9 years) and 12 383 participants of African ancestry (61.1% female; median age, 39.0 [range, birth-90.0 years]). A laboratory-wide association scan revealed a robust association between depression polygenic scores and white blood cell count (ß, 0.03; SE, 0.004; P = 1.07 × 10-17), which was replicated in a meta-analysis across the 4 health care systems (ß, 0.03; SE, 0.002; P = 1.03 × 10-136). Mediation analyses suggested a bidirectional association, with white blood cell count accounting for 2.5% of the association of depression polygenic score with depression diagnosis (95% CI, 2.2%-20.8%; P = 2.84 × 10-70) and depression diagnosis accounting for 9.8% of the association of depression polygenic score with white blood cell count (95% CI, 8.4%-11.1%; P = 1.78 × 10-44). Mendelian randomization provided additional support for an association between increased white blood count and depression risk, but depression modeled as the exposure showed no evidence of an influence on white blood cell counts. Conclusions and Relevance: This genetic association study found that increased depression polygenic scores were associated with increased white blood cell count, and suggests that this association may be bidirectional. These findings highlight the potential importance of the immune system in the etiology of depression and may motivate future development of clinical biomarkers and targeted treatment options for depression.

Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease.

BACKGROUND: Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and monitor disease conditions. Test results are stored in electronic health records (EHRs), and a growing number of EHRs are linked to patient DNA, offering unprecedented opportunities to query relationships between genetic risk for complex disease and quantitative physiological measurements collected on large populations. METHODS: A total of 3075 quantitative lab tests were extracted from Vanderbilt University Medical Center's (VUMC) EHR system and cleaned for population-level analysis according to our QualityLab protocol. Lab values extracted from BioVU were compared with previous population studies using heritability and genetic correlation analyses. We then tested the hypothesis that polygenic risk scores for biomarkers and complex disease are associated with biomarkers of disease extracted from the EHR. In a proof of concept analyses, we focused on lipids and coronary artery disease (CAD). We cleaned lab traits extracted from the EHR performed lab-wide association scans (LabWAS) of the lipids and CAD polygenic risk scores across 315 heritable lab tests then replicated the pipeline and analyses in the Massachusetts General Brigham Biobank. RESULTS: Heritability estimates of lipid values (after cleaning with QualityLab) were comparable to previous reports and polygenic scores for lipids were strongly associated with their referent lipid in a LabWAS. LabWAS of the polygenic score for CAD recapitulated canonical heart disease biomarker profiles including decreased HDL, increased pre-medication LDL, triglycerides, blood glucose, and glycated hemoglobin (HgbA1C) in European and African descent populations. Notably, many of these associations remained even after adjusting for the presence of cardiovascular disease and were replicated in the MGBB. CONCLUSIONS: Polygenic risk scores can be used to identify biomarkers of complex disease in large-scale EHR-based genomic analyses, providing new avenues for discovery of novel biomarkers and deeper understanding of disease trajectories in pre-symptomatic individuals. We present two methods and associated software, QualityLab and LabWAS, to clean and analyze EHR labs at scale and perform a Lab-Wide Association Scan.

Feasibility of Using a Nationally Representative Telephone Survey to Monitor Multiple Sclerosis Prevalence in the United States.

BACKGROUND: Multiple sclerosis (MS) is the most common chronic neurologic disease of young adults, placing a heavy burden on patients, families, and the healthcare system. Ongoing surveillance of the incidence and prevalence of MS is critical for health policy and research, but feasible options are limited in the United States and many other countries. We investigated the feasibility of monitoring the prevalence of MS using a large national telephone survey of the adult US population. METHODS: We developed questions to estimate the lifetime prevalence and age of onset of MS using the US-based Behavioral Risk Factor Surveillance System (BRFSS) and piloted these questions in 4 states (MN, RI, MD, and TX). There was a total of 45,198 respondents aged 18 years and above. Analyses investigated individual state and combined prevalence estimates along with health-related comorbidities and limitations. MS prevalence estimates from the BRFSS were compared to estimates from multi-source administrative claims and traditional population-based methods. RESULTS: The estimated lifetime prevalence of self-reported MS (per 100,000 adults) was 682 (95% CI 528-836); 384 (95% CI 239-529) among males and 957 (95% CI 694-1,220) among females. Estimates were consistent across the 4 states but much higher than recently published estimates using population-based administrative claims data. This was observed for both national results and for MS prevalence estimates from other studies within specific states (MN, RI, and TX). Prevalence estimates for Caucasian, African American, and Hispanic respondents were 824, 741, and 349 per 100,000 respectively. Age and sex distributions were consistent with prior epidemiologic reports. Comorbidity and functional limitations were more pronounced among female than male respondents. CONCLUSIONS: While yielding higher overall MS prevalence estimates compared to recent studies, this large-scale self-report telephone method yielded relative prevalence estimates (e.g., prevalence patterns of MS by sex, age, and race-ethnicity) that were generally comparable to other surveillance approaches. With certain caveats, population-based telephone surveys may eventually offer the ability to investigate novel disease correlates and are relatively feasible, and affordable. Further work is needed to create a valid question set and methodology for case ascertainment before this approach could be adopted to accurately estimate MS prevalence.

Effects of prenatal bacterial infection on cognitive performance in early childhood.

BACKGROUND: Previous epidemiologic studies have reported adverse neurodevelopmental sequelae following prenatal infectious exposure, yet long-term effects estimated from these observational studies are often subject to biases due to confounding and loss to follow-up. OBJECTIVES: We demonstrate the joint use of inverse probability (IP) treatment and censoring weights when evaluating neurotoxic effects of prenatal bacterial infection. METHODS: We applied IP weighting for both treatment and censoring to estimate the effects of maternal bacterial infection during pregnancy on mean intelligence quotient (IQ) scores measured at age 7 using the Wechsler Intelligence Scale for Children. Participants were members of a population-based pregnancy cohort recruited in the Boston and Providence sites of the Collaborative Perinatal Project between 1959 and 1966 (n = 11 984). We calculated average treatment effects (ATE) and average treatment effects on the treated (ATT) using IP weights estimated via generalized boosted models. RESULTS: ATE- and ATT-weighted mean IQ scores were lowest among offspring exposed to multi-systemic bacterial infection during pregnancy and highest for those unexposed. The effects of prenatal bacterial infection were greater among male offspring, particularly on performance IQ scores. Offspring who were exposed to multi-systemic bacterial infection in the third trimester displayed the largest reduction in mean full-scale, verbal, and performance IQ scores at age 7 compared to those unexposed or exposed in earlier trimesters. CONCLUSIONS: We find that prenatal bacterial infection is associated with cognitive impairments at age 7. Associations are strongest for more severe infections, that occur in the third trimester, and among males. Public health intervention targeting bacterial infection in pregnant women may help enhance the cognitive development of offspring.

Maternal Bacterial Infection During Pregnancy and Offspring Risk of Psychotic Disorders: Variation by Severity of Infection and Offspring Sex.

OBJECTIVE: Previous studies suggest that prenatal immune challenges may elevate the risk of schizophrenia and related psychoses in offspring, yet there has been limited research focused on maternal bacterial infection. The authors hypothesized that maternal bacterial infection during pregnancy increases offspring risk of psychotic disorders in adulthood, and that the magnitude of this association varies as a function of severity of infectious exposure and offspring sex. METHODS: The authors analyzed prospectively collected data from 15,421 pregnancies among women enrolled between 1959 and 1966 at two study sites through the Collaborative Perinatal Project. The sample included 116 offspring with confirmed psychotic disorders. The authors estimated associations between maternal bacterial infection during pregnancy and psychosis risk over the subsequent 40 years, stratified by offspring sex and presence of reported parental mental illness, with adjustment for covariates. RESULTS: Maternal bacterial infection during pregnancy was strongly associated with psychosis in offspring (adjusted odds ratio=1.8, 95% CI=1.2-2.7) and varied by severity of infection and offspring sex. The effect of multisystemic bacterial infection (adjusted odds ratio=2.9, 95% CI=1.3-5.9) was nearly twice that of less severe localized bacterial infection (adjusted odds ratio=1.6, 95% CI=1.1-2.3). Males were significantly more likely than females to develop psychosis after maternal exposure to any bacterial infection during pregnancy. CONCLUSIONS: The study findings suggest that maternal bacterial infection during pregnancy is associated with an elevated risk for psychotic disorders in offspring and that the association varies by infection severity and offspring sex. These findings call for additional investigation and, if the findings are replicated, public health and clinical efforts that focus on preventing and managing bacterial infection in pregnant women.