JAMA Network Open

Objective: To estimate the adjusted relative risk (RR) of administrative grant disruption faced by first-time and mechanism-first principal investigators (PIs) during the 2025 National Institutes of Health (NIH) grant disruptions. Design: Retrospective cohort study linking NIH RePORTER data to a publicly curated registry of grants disrupted in 2025. Setting: All NIH active research grants in fiscal years 2024 to 2025. Participants: 80,976 active projects: 4,961 disrupted during the wave that peaked in May 2025, 76,015 non-disrupted controls. Main outcome measures: Adjusted RR of disruption by two pre-specified first-time PI constructs: absolute first-time PI (no prior NIH grant) and mechanism-first PI (no prior NIH grant with the same activity code). Modified Poisson regression with institution-clustered standard errors adjusted for project, institutional, and geographic covariates. A pre-specified fiscal year 2024 common-anchor analysis addressed year-of-disruption confounding. Results: Of 4,961 disrupted grants, 237 (4.8%) had an absolute first-time PI and 396 (8.0%) had a mechanism-first PI. After adjustment, absolute first-time PIs faced 77% elevated risk of disruption (RR 1.77, 95% CI 1.34 to 2.32) and mechanism-first PIs faced 57% elevated risk (RR 1.57, 1.16 to 2.11). Under the common-anchor analysis, the absolute first-time effect attenuated to RR 1.22 (0.95 to 1.58); the mechanism-first effect persisted (RR 1.48, 1.07 to 2.06). The elevated risk was concentrated in research-mechanism grants (RR 1.78, 1.26 to 2.52) and was robust across 8 of 9 pre-specified sensitivity analyses. The Track A start-time construct, which asks whether the disrupted project was the PI's debut grant, yielded null estimates (RR 0.98, 0.93 to 1.04), with any effect concentrated entirely in newly started projects. Conclusions: First-time and mechanism-first PIs faced disproportionately elevated risk of disruption during the 2025 NIH wave, concentrated in research-mechanism grants and robust to year-confounding-free identification. The relevant exposure was being early-career at the moment of administrative action, not at project initiation. The findings have direct implications for workforce equity in US biomedical research.

ObjectivesTo examine the extent to which heat-related causes of death are recorded in fatal drug overdoses, how these patterns vary across states and over time, and how overdose characteristics differ between deaths with, versus without, heat involvement recorded. MethodsDeath certificate data for all drug overdose deaths in US residents from 2001 to 2024 (from the National Center for Health Statistics) were analyzed to identify whether a heat-related cause of death was also listed on the death certificate. Joinpoint regression, descriptive statistics, and nonparametric tests were used to examine temporal trends and compare overdose deaths with versus without recorded heat involvement. ResultsIn 2001, fewer than 10 drug overdose deaths with recorded heat involvement were identified, but this number increased to 558 in 2024. From 2013 to 2024, mortality rates increased significantly, with an estimated annual percent change of 30.1 (95% Confidence Interval, 26.5-47.1). The highest mortality rates and numbers of deaths were observed in residents of Arizona and Nevada. American Indian/Alaska Native, Mexican-heritage, and foreign-born populations accounted for larger shares of overdose deaths with, compared to without, heat involvement recorded. A street or highway was more frequently identified as the place of injury in overdose deaths with (18.9%), versus without (2.2%) heat involvement reported. Psychostimulants such as methamphetamine were involved in 85.9% of overdose deaths with, compared to 28.9% without, recorded heat involvement. ConclusionsAlthough representing only a fraction of all overdose deaths, fatal overdoses involving heat exposure have increased markedly over time and disproportionately impact certain states and demographic groups.

Introduction: Even though state minimum wage (MW) is a policy lever that affects income and poverty and can prevent of violence, no prior study has comprehensively evaluated its impact in the United States (US). In this study, we estimated the impact of at least a $1 USD increase in state MW above the federal MW on overall, firearm, and non-firearm homicide mortality and examined its impact on racialized inequities. Methods: We conducted a quasi-experimental study using controlled interrupted time series (CITS) and synthetic controlled interrupted time series (SCITS) approaches to examine immediate and sustained impact of state MW increases. We used state-month level homicide victimization mortality data from 2010-2019. Homicide victimization death was identified using International Classification of Disease codes, 10th revision. State MW data was obtained from the Bureau of Labor Statistics. Results: Demographic and social variables from intervention, never-exposed, and always-exposed states were similar to each other and representative of the total US population from all 50 states. The CITS results show that after MW increases in the intervention states, these states experienced a sustained decline of -0.22 (-0.37, -0.07) homicide victimizations/ 100,000 person-years/ year relative to the never-exposed states and -0.39 (-0.59, -0.18) relative to always-exposed states. This resulted in 5,657 fewer homicide victimization deaths in the intervention states over four years of post-MW increase period compared to the never-exposed states. SCITS results were similar to the CITS results, and the majority of sustained declines were observed in firearm-related deaths and among Black population. Conclusion: MW increase was associated with a reduction in homicide victimization rates, which were robust in multiple sensitivity analyses, more pronounced for firearm-related homicide deaths, and reduced homicide victimization inequities for Black Americans.

Background: Store-and-forward teledermatology commonly relies on several patient-submitted photographs of the same concern, but most dermatology artificial intelligence models classify single images independently. Objective: To develop and internally validate a case-level diagnostic-support model that aggregates multiple patient-submitted photographs for common dermatologic conditions. Methods: We conducted a retrospective diagnostic-modeling study using the Skin Condition Image Network, a public dataset of deidentified self-taken dermatology images from US adults. We curated 2,336 cases comprising 5,041 images across 10 common inflammatory, allergic, and infectious conditions. Cases were split at the submission level into training, validation, and held-out test sets. Frozen general-purpose and dermatology-specific encoders were compared with image-level classifiers and a gated-attention multiple instance learning model that generated one case-level output from 1-3 images. Results: The strongest image-level baseline, dermatology-specific embeddings with random forest classification, achieved macro/micro ROC-AUCs of 0.797/0.854. Case-level aggregation improved discrimination, with dermatology-specific embeddings plus multiple instance learning achieving mean macro/micro ROC-AUCs of 0.819/0.863 across repeated stratified experiments. The locked final model achieved macro/micro ROC-AUCs of 0.800/0.849 on the held-out test set. Balanced-threshold sensitivity/specificity examples were 0.702/0.688 for eczema and 0.818/0.826 for urticaria. Limitations: Internal validation used a 10-condition subset from a US volunteer dataset; external validation, calibration, subgroup performance analysis, and prospective workflow studies are required. Conclusion: Modeling the teledermatology submission as a multi-image case better reflects asynchronous dermatology workflow than single-image classification. The model is preliminary clinician-facing support for structured review and triage, not autonomous diagnosis.

Importance: Problematic use of the internet (PUI) behaviors, including problematic gaming, social media use, smartphone use, and general internet use, have been increasingly studied worldwide. So far, it is unclear what the global prevalence of PUI is. Objective: To critically appraise existing systematic reviews and meta-analyses on the prevalence of PUI behaviors and generate aggregated global prevalence estimates across different manifestations and definitions. Data Sources: MEDLINE (Ovid), Embase (Ovid), Scopus, Web of Science, CINAHL, and the Cochrane Review Library were searched for relevant articles from database inception to the most recent available search prior to manuscript preparation. Searches targeted systematic reviews and meta-analyses reporting prevalence for PUI-related behaviors. Study Selection: Systematic reviews and meta-analyses of observational studies reporting prevalence estimates for problematic gaming, problematic internet use, problematic smartphone use, problematic social media use, or sexting were included. Scoping reviews were retained for descriptive synthesis only. Data Extraction and Synthesis: An umbrella review methodology was used. Data extraction and methodological appraisal were conducted using AMSTAR-2 to assess the quality of included systematic reviews up to February 2026. Primary studies included in each review were extracted and pooled using random-effects meta-analysis. Analyses were conducted to estimate pooled prevalence with 95% confidence intervals (CIs) and heterogeneity across non-overlapping primary studies. Small-study effects were examined. Main Outcomes and Measures: Global pooled prevalence estimates for PUI behaviors, including problematic gaming, problematic internet use, problematic smartphone use, problematic social media use, and sexting. Results: Eleven reviews, including 10 systematic reviews and 1 scoping review, met inclusion criteria, representing data from 3,145,428 individuals, of whom 3,030,023 were included in pooled prevalence analyses. Across regions, pooled prevalence estimates were 6% (95% CI, 5%-7%) for problematic gaming, 16% (95% CI, 15%-17%) for problematic internet use, 32% (95% CI, 28%-35%) for problematic smartphone use, and 23% (95% CI, 19%-28%) for problematic social media use. Substantial heterogeneity (I2 > 99%) was observed across primary studies, reflecting variation in study methodologies, sampled populations, and definitions of PUI behaviors. Conclusions and Relevance: PUI behaviors appear to affect a substantial proportion of the global population. However, methodological concerns were common, with 9 of 10 systematic reviews rated as having low or critically low confidence according to AMSTAR-2. Evidence remains concentrated in East Asia and Europe, and many reviews combine heterogeneous populations and sampling strategies. Additional high-quality epidemiological research, including studies in underrepresented regions, is needed to refine prevalence estimates, clarify risk factors, and support the development of standardized criteria for PUI behaviors.

Importance BRCA genetic testing is critical for cancer risk assessment, treatment and personalization, yet substantial underutilization persists. Socioeconomic and clinical factors may strongly influence testing uptake; therefore, identifying the potential drivers to BRCA testing and treatment is essential for addressing gaps in access, increasing retention into care, and improving cancer outcomes. Objective To quantify the putatively causal effects of SDoH on BRCA genetic testing among individuals with breast, ovarian, pancreatic, and prostate cancers and to develop a predictive model to identify patients at risk for underuse of testing. Design, Setting, and Participants This observational case-control study used data from a large multistate clinical research data network covering southern US (2012-2023). The network contained records of more than 26 million individuals and was linked with ZIP code-level SDoH variables derived from national socioeconomic datasets. Adults diagnosed with breast, ovarian, pancreatic, or prostate cancer were eligible for cases (received BRCA testing) or controls (did not receive BRCA testing, matched by cancer diagnosis). Exposure SDoH categories, including economic conditions, education, healthcare access, neighborhood conditions, and social connectedness. Main Outcomes and Measures The primary outcome was receipt of BRCA genetic testing after cancer diagnosis. Results Among 3,279 people diagnosed with cancer, 748 received BRCA testing and 2,531 served as controls. Study population mean [SD] age was 66.8 [15.7] years; 1,758 were women [53.6%], 2,238 [69.6%] were White and 616 [18.8%] were Black or African American. Breast (1,420 [42.8%]) and prostate (1,342 [40.9%]) cancers were the most common diagnoses, followed by pancreatic (242 [7.4%]), ovarian (238 [7.2%]), and multiple cancers (55 [1.7%]). Upon adjusting for potential confounding, higher educational attainment (odds ratio [OR], 1.19), public-sector employment (OR, 1.42), neighborhood safety (OR, 1.28), and social participation (OR, 1.72) showed an increased likelihood of undergoing BRCA testing, whereas economic instability, including housing cost burden and reliance on public insurance, had an effect of reduced testing. A random forest classifier demonstrated good discriminative performance (AUROC, 0.776) to predict cancer patients who were likely to take BRCA testing, where nativity, language, and residential stability ranked among the most influential social determinants according to SHapley Additive exPlanations (SHAP) analysis. Conclusions and Relevance In this observational case-control study, SDoHs were strongly associated with receipt of BRCA genetic testing among people with cancer. These findings suggest that disparities in genetic testing may reflect structural and social barriers rather than differences in clinical eligibility alone. Efforts to improve equitable access to genetic testing may benefit from integrating social-context information into clinical workflows and targeting outreach or navigation strategies toward socially disadvantaged populations.

Purpose: Adjuvant endocrine therapy (AET) substantially reduces recurrence and mortality in hormone receptor-positive breast cancer but requires sustained daily adherence over 5 to 10 years. Approximately one-third of patients fall short of recommended adherence in the first year alone, largely due to distinct combinations of attitudes, barriers, and circumstances. Existing studies have catalogued individual risk factors but lack the scale and breadth to characterize how these factors co-occur within patients, or to distinguish behavioral drivers from confounding by clinical and demographic context. We sought to characterize the behavioral and social heterogeneity underlying AET adherence in a national real-world cohort. Moving beyond population-average risk factors, we identify the distinct patient profiles, and the differing drivers within them, that any effective adherence strategy must address. Methods: We conducted a retrospective cohort study of US women with invasive breast cancer diagnosed between 2016 and 2025, linking two large-scale, individual-level datasets through privacy-preserving tokenization: Surgo Health's BehavioralPulse, which provides modeled individual-level behavioral and attitudinal risk scores together with consumer sociodemographic attributes, and longitudinal medical and pharmacy claims from a claims data provider. Eligible patients underwent 1 to 2 breast surgeries, initiated oral AET (tamoxifen or aromatase inhibitors), and maintained continuous insurance coverage for 365 days following therapy initiation. The primary outcome was adherence, defined as medication possession ratio (MPR) [≥]80% in the first year. Mixed-effects logistic regression with a random intercept for ZIP3 estimated adjusted associations across behavioral, sociodemographic, and clinical predictors. To characterize how behavioral factors co-occur within patients, we identified the most prevalent configurations of the statistically significant behavioral predictors and estimated their relative association with adherence, holding clinical and demographic factors constant. Results: The final cohort included 401,450 women, of whom 280,595 (69.9%) achieved MPR [≥]80%. Several behavioral factors were independently associated with adherence after adjustment for clinical and demographic covariates, including comfort following medication instructions (aOR, 1.15; 95% CI, 1.06-1.24), geographic proximity to breast oncologists (aOR, 1.17; 95% CI, 1.04-1.32), tangible instrumental social support (aOR, 1.06; 95% CI, 1.00-1.13), religiosity (aOR, 1.04; 95% CI, 1.01-1.08), concern about sexual side effects (aOR, 0.96; 95% CI, 0.93-0.99), and cost-related access barriers (aOR, 0.97; 95% CI, 0.95-1.00). The 10 most common configurations of significant behavioral predictors accounted for over 70% of the cohort, with the two most prevalent representing more than 40% of patients. The most common profile, defined by an absence of behavioral barriers and the presence of social support, was associated with a positive behavioral contribution to adherence propensity (behavioral linear predictor OR = 1.18; 95% CI: 1.04-1.36) comparable in magnitude to several established clinical predictors. Compared against this referent profile, six of the nine remaining profiles had lower adherence, with relative odds ranging from approximately 0.92 (95% CI: 0.89-0.95) to 0.97 (95% CI: 0.94-0.99). One profile, similar to the reference but including high trust in doctors, was associated with higher adherence odds (1.04, 95% CI: 1.01-1.07). These profiles arose from substantively different underlying combinations of factors: segments dominated by cost barriers, by side-effect concerns, or by limited social support produced comparable overall adherence risk but through distinct pathways. Conclusion: In this national cohort, nearly one-third of women did not achieve recommended first-year adherence to AET. The pathways to non-adherence were heterogeneous, structured into recurring behavioral profiles rather than randomly distributed across patients. This heterogeneity is clinically meaningful: patients with similar adherence risk may benefit from substantially different forms of support, ranging from financial navigation to side-effect management to social support resources. Surfacing this structure required linking individual-level behavioral data to large-scale claims data, offering a practical foundation for optimal design of patient-centered adherence interventions that are tailored to the specific configurations of barriers patients actually face.

Background: Polygenic risk scores (PRS) for breast cancer are increasingly used for risk stratification to inform screening and prevention. However, for PRSs to be equitable and clinically useful, they need to perform well across diverse populations. While PRS performance is known to be ancestry-dependent, it is not well understood how environmental context, such as that of socioeconomic status (SES), affects PRS transferability. Here, we assess whether SES, measured via self-reported household income, modifies breast cancer PRS performance and, if so, whether socioeconomic context contributes predictive information beyond genetic risk alone. Methods: We used the US-based All of Us biobank to evaluate how SES impacts breast cancer PRS performance. First, we quantified changes in breast cancer PRS performance by modeling a commonly-cited polygenic score for breast cancer previously described by Mavaddat et al. with SES. We then reestimated the genetic effect sizes of the 3,820 variants from Mavaddat et al. in All of Us with and without income as a covariate. Because social determinants of health affect breast cancer detection and outcomes, we stratified analyses by socially defined populations on the basis of self-identified race and ethnicity. We further stratified individuals whose self-identified race is White (''White'') into three SES groups (high, middle, low) based on self-reported income and re-estimated genetic effect sizes to create SES-specific PRSs. We then applied these PRSs to White participants, the largest group in the study, and to Black or African American (''Black'') and Hispanic or Latino (''Hispanic'') participants, groups underrepresented in breast cancer research. Model discrimination between cases and controls was measured by area under the curve (AUC). Results: We analyzed 163,715 women from the All of Us biobank, which included 8,833 breast cancer cases (6,619 White, 1,178 Black, and 1,036 Hispanic), with relative income available for a subset of these cases (5,525 White, 848 Black, and 566 Hispanic). The ancestry-dependent performance of the breast cancer PRS described in Mavaddat et al. was replicated in All of Us. In Black individuals, this PRS (AUC and 95% CI: 0.576 [0.571, 0.582]) produced a similar increase in AUC as relative income (AUC: 0.573 [0.568, 0.577]) when added to an age-only model. Incorporating income with PRS, age, and genetic PCs 1-3 improved AUC by 0.007 in White Americans and 0.018 in Black Americans (both p < 10-11), while attenuating the contribution of PRS in the full model. PRS performance also varied among SES categories. Notably, PRSs with variant effect sizes that were recalibrated in low-SES White participants performed best in low-SES White participants (AUC: 0.605 [0.583, 0.628]) and Black Americans (AUC: 0.588 [0.586, 0.591]), both better than performance in high-SES White Americans (AUC: 0.579 [0.577, 0.580]) and middle-SES White Americans (AUC: 0.578 [0.569, 0.586]). Conclusion: Socioeconomic context, measured by income, significantly impacts the transferability of a PRS for breast cancer within and among groups defined by self-identified race and ethnicity. Accounting for SES improves PRS performance, most notably in Black Americans and low-SES White individuals.

Background: Prenatal substance exposure (PSE) occurs when an individual is exposed to substances in utero. PSEs may have lasting effects on mental health. We tested whether PSEs show threshold, cumulative, or individual substance associations with childhood psychiatric diagnoses. Methods: Clinical variables (demographics, ICD-9/10 diagnoses, PSE history) were extracted from electronic health records from the University of Minnesota Adoption Medicine Clinic. PSEs were identified from caregiver and child-protective-services narratives and/or toxicology (cord tissue/blood, meconium). For each ICD-9/10 diagnostic category, we fit logistic regression models comparing (1) exposure thresholds (0, 1, 2, 3, 4+ exposures), (2) a cumulative exposure count, and (3) individual substances to estimate marginal odds ratios (ORs) with 95% Confidence Intervals (CIs). Results: Psychiatric diagnoses increased with the number of PSEs. Relative to no exposure, odds of an Anxiety Disorder rose from OR 1.47 (95% CI 1.16-1.87) with one exposure to OR 2.03 (1.64-2.52) with >=4 exposures. Higher cumulative exposure scores were associated with Anxiety Disorders (OR 1.28, 1.18-1.38), Behavioral and Emotional Disorders (OR 1.42, 1.31-1.54), Substance Use Disorders (OR 1.52, 1.29-1.79), and Mood Disorders (OR 1.16, 1.04-1.30). Alcohol, tobacco, and marijuana exposures were associated with increased odds of at least one psychiatric diagnosis, and each substance showed at least one significant diagnostic cluster when modeled independently. Conclusion: Increasing numbers of PSEs were associated with higher odds of psychiatric diagnoses, with patterns varying by substance and outcome. These findings motivate research on exposure timing and combinations to support earlier identification and intervention for at-risk children.

Background: In 2024, the BMJ updated its data-sharing policy for clinical trials, requiring deidentified individual patient data (IPD) to be openly deposited prior to publication. Our objective was to discover if data-sharing increased after introduction of the new policy. Method: All data-sharing statements were downloaded from BMJ trials published in 2023 (submitted pre-updated policy) and 2025 (submitted post-updated policy). Data for 2025 were gathered for trials in five comparison medical journals. Data-sharing statements were coded to specify whether IPD were immediately available, and if not, the reason why. Where a statement gave a link to a repository, we checked whether data were available. Results: Openly available IPD for BMJ trials increased from 0/32 prior to the new policy to 19/33 (58%) after the updated policy; seven articles gave repository links that did not yield any data. In the five comparison journals, rates of open IPD varied from 0% to 5.6%. Conclusions: There was a substantial increase in open sharing of IPD after introduction of the new policy compared to a prior period. Open sharing of IPD is possible, but it is unpopular with authors and is unlikely to be achieved without firm editorial enforcement

Background Preterm birth (PTB) rates among Hispanic/Latina individuals in the United States have risen over the past decade. Data suggests this rise may be driven in part by psychosocial stress. Leukocyte telomere length (LTL), a marker of cumulative cellular aging that shortens under chronic stress, may capture stress-related biological vulnerability, but has not been examined as a potential population-level contributor to PTB in Hispanic/Latina pregnancies. Objective To examine the association between mid-pregnancy maternal LTL and PTB in a population-based Hispanic/Latina cohort. Methods In a case-control study nested within a California singleton birth cohort (n = 436 Hispanic/Latina individuals; 215 PTB, 221 term births), LTL was measured by quantitative PCR from biobank specimens collected from 15 to 20 weeks of gestation. Covariates from linked birth certificate and hospital discharge records were included. Logistic regression estimated ORs and 95% CIs of PTB by LTL examined continuously and by percentile category (<=10th, 11th-89th, >=90th) with and without adjustment for covariates. Results Mean and median LTL did not differ between PTB and term births. LTL at or below the 10th percentile was associated with elevated odds of PTB relative to full-term birth (12.6% versus 4.3%; ORc = 3.2, 95% CI 1.3-7.9), persisting after partial (ORadj1 = 3.2, 95% CI 1.3-8.3) and full covariate adjustment (ORadj2 = 3.4, 95% CI 1.3-9.3). Subgroup analyses showed consistent directional patterns across PTB subgroups and for early term birth (ORadj2 = 5.1, 95% CI 1.5-17.0). Conclusions Mid-pregnancy maternal LTL <=10th percentile was associated with more than three times the odds of PTB, with risk concentrated at the extreme low tail of the distribution. Consistent with a cumulative allostatic load model, markedly short LTL at mid-gestation may reflect elevated stress-related biological risk for preterm delivery. These findings support upstream investment in stress reduction and prospective LTL research in high-burden populations.

PurposeOpioid overdose deaths disproportionately affect racial and ethnic minority populations in the United States, yet claims-based evidence characterizing the multi-dimensional structure of these disparities across incidence, treatment access, costs, and insurance coverage remains limited. MethodsWe conducted a retrospective cross-sectional and longitudinal cohort analysis using the HealthVerity Launch Sample, a large administrative claims database. The study population comprised 3,675,823 patients across 5 racial groups enrolled between 2020 and 2024. Eight primary analyses were conducted, including age-sex standardized overdose rates, temporal disparity trends, medication-assisted treatment (MAT) receipt, naloxone access, pharmacy costs, insurance payer type, care setting, and multivariable logistic regression for overdose risk. ResultsBlack patients had the highest age-sex standardized overdose rate (363.4 per 100,000; rate ratio [RR] = 1.27 vs. White), and those with opioid use disorder (OUD) received MAT at a rate 35% lower than White patients (19.8% vs. 30.7%; RR = 0.645), driven primarily by a buprenorphine access deficit. AIAN patients demonstrated consistent multi-dimensional disadvantage across naloxone access, MAT engagement, and pharmacy costs. After adjustment for payer type, age, and sex, all non-White groups showed lower adjusted odds of overdose than White patients (Black OR = 0.87; AIAN OR = 0.25), with Medicaid enrollment carrying 7.06 times the overdose odds of commercial insurance. ConclusionInsurance type is the dominant predictor of overdose risk, and the disproportionate Medicaid enrollment of Black patients is both a consequence of structural disadvantage and access disparities. Targeted interventions such as buprenorphine expansion in Medicaid and enhanced naloxone distribution are recommended.

Background: Adverse childhood experiences (ACEs) are traumatic or adverse events in early life that can have lasting effects on behavioral, emotional, and psychological functioning. Prior research suggests ACEs relate to later psychiatric outcomes through threshold, cumulative, and individual-specific risk patterns. Few studies, however, have operationalized all three models to test ACE-specific associations with diagnosed psychiatric disorders in individuals who are adopted or with foster care histories. Methods: We conducted a cross-sectional retrospective study using electronic health record data from foster care and adopted patients aged 0-21 years old seen at the University of Minnesota Adoption Medicine Clinic (UMN-AMC) between 2014-2024. Extracted measures included ACE history, demographics, and psychiatric diagnoses. We used latent class analysis and logistic regression to identify clusters of adversity and estimate associations with psychiatric diagnosis domains, adjusting for Sex and Age at Initial Visit. Results: ACEs showed a threshold pattern across psychiatric domains, with higher ACE counts associated with greater odds of psychiatric diagnoses. Individual risk modeling indicated that exposure to abuse or violence was associated with higher odds of psychiatric diagnoses. Across cumulative and individual risk approaches, Anxiety Disorders, Mood Disorders, and Behavioral or Emotional Disorders showed the greatest sensitivity to adversity. Conclusion: Current ACE models may not fully capture neurodevelopmental impacts reflected in diagnosed psychiatric disorders among adolescents, particularly in high-risk groups such as foster and adopted individuals. In a large clinic sample our findings support a nuanced association between ACEs and later psychiatric diagnoses and highlight the need for ACE-focused assessment, prevention, and treatment strategies tailored to foster care and adopted populations.

Background: The global SARS-CoV-2 pandemic disrupted healthcare systems worldwide, raising concerns about its impact on clinical research. Early reports suggested reductions in participant enrollment, interruptions to ongoing trials, and challenges to protocol adherence, yet the magnitude and duration of these operational disruptions remain unclear. Methods: We conducted a registry-based analysis comparing clinical trials during the COVID-19 pandemic (December 2019 to November 2022) with a matched pre-pandemic cohort (December 2016 to November 2019). Studies were included if they reported any modifications to trial status, enrollment, or protocols during the study periods. Key variables included trial stoppage, enrollment changes, and adoption of remote or hybrid procedures. Results: The global SARS-CoV-2 pandemic resulted in widespread disruptions to trial operations with 13,323 clinical trials terminated, suspended or withdrawn over the course of the pandemic, a 38% increase compared to the 9,665 trials that stopped in the 3 years prior to the pandemic. Registries indicated a sharp decline in new participant enrollment across geographic regions and therapeutic areas, with partial recovery in later months. Review findings highlighted barriers including patient inaccessibility, staff redeployment, and supply chain interruptions. Conclusions: The pandemic caused system-wide operational shocks that compromised trial timelines and may have downstream methodological consequences. Recovery in enrollment does not imply restoration of pre-pandemic protocol fidelity or outcome ascertainment. Standardized reporting of disruptions, proactive contingency planning, and resilient trial designs are needed to maintain data integrity during large-scale disruptions and to support reliable evidence generation.

Background: The Vaccine Safety Datalink (VSD) detected a statistical signal for ischemic events (ischemic stroke or transient ischemic attack) following bivalent mRNA COVID-19 vaccination through prospective surveillance during 2022-2023. Although multiple studies from other surveillance systems and countries reported no increased risk, important methodological limitations remained. This U.S. study addressed those limitations by evaluating the ischemic stroke risk following bivalent mRNA COVID-19 vaccination, influenza vaccination, and their same-day coadministration using event-dependent self-controlled case series (SCCS) design. Methods: Study outcomes included first-ever ischemic stroke (primary outcome), first-in-1-year ischemic stroke (secondary outcome), and ischemic events (exploratory outcomes), identified using ICD-10-CM codes in inpatient and emergency department settings during September 1, 2022-March 31, 2023, among individuals aged>=12 years across eight VSD sites. Analyses were conducted separately for Pfizer-BioNTech and Moderna bivalent vaccines, with relative incidences (RI) and 95% confidence intervals (CI) estimated for 1-21-day and 1-42-day risk intervals, using person-time outside these intervals as the control period. Subgroup analyses were performed by age group (12-64, >65 years) and history of documented SARS-CoV-2 infection. Results: A total of 6,510 first-ever ischemic strokes were identified among more than 6.8 million participants. Among recipients of Pfizer-BioNTech bivalent COVID-19 and influenza vaccines, no statistically significant increased risk of first-ever ischemic stroke was observed following bivalent COVID-19 vaccination (RI=0.94; 95% CI: 0.63-1.41), influenza vaccination (RI=0.95; 95% CI: 0.82-1.10), or same-day coadministration (RI=1.15; 95% CI: 0.88-1.49) within 1-21-day risk intervals; findings were similar for 1-42-day intervals. Comparable null results were observed for Moderna vaccines and across all subgroups, secondary, and exploratory outcomes. Conclusion: No increased risk of ischemic stroke was found following bivalent mRNA COVID-19 vaccination, influenza vaccination, or their coadministration in this multi-site SCCS study. These findings are consistent with previous studies and underscore the importance of continued vaccine safety monitoring.

Background: Observational studies have consistently reported associations between social media use (SMU) and poorer mental health outcomes; however, such designs cannot establish causality. This study synthesised evidence from randomized experiments to estimate the effects of restricting SMU on mental health outcomes. Methods: A systematic search was conducted across MEDLINE, Embase, PsycINFO, and Cochrane CENTRAL to identify experimental trials evaluating interventions that constrained SMU for at least 24 hours and included an unconstrained control condition. Multilevel random-effects meta-analyses were used to synthesise effect estimates. Prespecified meta-regressions explored study-level moderators, and population-level impact fractions were estimated relative to global SMU prevalence. Results: From 7,784 screened records, 37 reports representing 35 distinct studies were included (pooled N = 7,160). Most interventions lasted one to three weeks and targeted college-aged youth. Pooled estimates favoured SMU constraints across outcomes, with magnitude and precision varying by domain. Confidence intervals were entirely above zero, consistent with a beneficial response for depressive symptoms (g = 0.22; 95% CI, 0.12 to 0.32), perceived stress (g = 0.15; 95% CI, 0.01 to 0.29), anxiety symptoms (g = 0.19; 95% CI, 0.05 to 0.34), fear of missing out/nomophobia (g = 0.14; 95% CI, 0.04 to 0.24), and well-being (g = 0.36; 95% CI, 0.10 to 0.63). Heterogeneity was substantial for several outcomes (I2 > 75%). In bivariate meta-regressions, higher baseline SMU was associated with larger effects for anxiety symptoms ({beta} = 0.13; 95% CI, 0.03 to 0.22), and longer interventions were associated with larger effects for depressive symptoms ({beta} = 0.16; 95% CI, 0.02 to 0.30). Inferences revealed that a short-term reduction in SMU globally could plausibly mitigate 17.5% and 15.4% of depressive and anxiety symptom cases, respectively. Conclusions: Experimental design-based evidence supports the causal case for an effect of SMU on mental health, with constraints producing improvements across multiple outcomes and no evidence of harm. Population-level inferences suggest that even individually modest effects may translate into meaningful public health benefits given the high prevalence of SMU exposure. These findings suggest that reducing SMU may represent a low-intensity, low-cost, scalable strategy to support mental health and improve well-being.

Background: Cannabis use is highly prevalent among emerging adults (18-25 years), a developmental period marked by ongoing neurodevelopment and heightened risk for cannabis use disorder (CUD). Structural alterations in the orbitofrontal cortex (OFC) and medial prefrontal/anterior cingulate cortex (mPFC/ACC) have been linked to cannabis use, though findings remain inconsistent in directionality. To address this, we examined cortical thickness and surface area of the OFC and mPFC/ACC subregions using the high-resolution Glasser atlas, allowing for more granular characterization of associations with CUD severity. Method: One hundred eleven emerging adults (41% male, aged=20.6{+/-}1.1 years) reporting significant alcohol and/or cannabis use completed clinical assessments and structural MRI. The OFC and mPFC/ACC were segmented into seven and six subregions per hemisphere, respectively. Multiple linear regressions tested associations between cortical thickness or surface area and DSM-5 CUD symptom count, controlling for alcohol use and intracranial volume. Subregions surviving false discovery rate correction were examined in relation to depression, trauma-related symptoms, impulsivity, and cannabis use motives. Results: Greater CUD severity was associated with lower cortical surface area and greater cortical thickness in OFC and mPFC/ACC subregions. Lower OFC surface area was correlated with coping- and enhancement-related cannabis use motives. Lower mPFC/ACC surface area and greater thickness were associated with more severe depression, trauma-related symptoms, and impulsivity. Conclusion: In high-risk emerging adults, greater CUD symptom burden is associated with lower surface area and greater thickness in OFC and mPFC/ACC subregions. Using the high-resolution Glasser atlas, these findings provide a more precise characterization of structural correlates of CUD and highlight potential neurobiological markers linked to affective and motivational processes underlying cannabis use.

Background: With growing impetus to integrate artificial intelligence (AI) tools into radiology, clinical practices must navigate workflow redesign. This carries implications for medical malpractice liability. Methods: We conducted an online vignette experiment with United States adults who acted as hypothetical jurors in a malpractice case involving a missed intracranial hemorrhage. Participants (n=2,347) were randomized to one of 22 conditions: a no-AI control and 21 conditions involving a hypothetical AI system. These twenty-one conditions varied by whether (1) a single-read or double-read workflow was used, (2) the radiologist's initial interpretation was documented, (3) the radiologist changed their interpretation after viewing AI output, (4) the AI detected the abnormality, and (5) the AI error rate--False Discovery Rate (FDR) or False Omission Rate (FOR--was provided to participants only, both participants and radiologist, or neither. The primary outcome was perceived liability, assessed by whether the radiologist met their duty of care. Findings: Perceived liability differed across conditions (p<0.0001). Double-read workflows (p<0.0001), documenting initial interpretations (p=0.0125), and providing participants with AI error rates, including the FDR (p=0.0038) or FOR (p=0.0035), reduced perceived liability. Liability was also lower when AI was incorrect (p<0.0001). Radiologists' awareness of AI error rates did not significantly impact liability. Notably, we observed an erroneous change penalty: the greatest liability occurred when radiologists initially identified an abnormality but later changed their interpretation to normal after seeing that AI identified the case as normal; conversely, perceived liability was lowest with documented, double-read workflows. Interpretation: Double-read workflows with documented initial interpretations and disclosure of AI error rates reduce perceived liability, though changing a correct initial interpretation increases it. Strategic workflow design is critical for successful AI implementation that can mitigate malpractice risk.

Importance: Hereditary breast and ovarian cancer (HBOC) variant carriers benefit from risk-reducing interventions, but only if identified. The extent to which carriers are clinically recognized, and whether recognition is equitable across diverse populations, is poorly characterized in a single large U.S. cohort. Objective: To estimate P/LP HBOC carrier prevalence across genetic ancestry groups, quantify documented clinical genetic testing among carriers, and evaluate ancestry and socioeconomic disparities in testing. Design, Setting, and Participants: Cross-sectional analysis of the All of Us Research Program Controlled Tier (Curated Data Repository v8/C2024Q3R9), comprising participants with short-read whole genome sequencing and linked electronic health record (EHR) and survey data. Carriers were ascertained from research genomic data independent of clinical testing. Exposures: Genetically inferred ancestry (African [AFR], Admixed American [AMR], East Asian [EAS], European [EUR], Middle Eastern [MID], South Asian [SAS]); self-reported household income and educational attainment. Main Outcomes and Measures: (1) Carrier prevalence with Wilson 95% CIs; (2) documented clinical genetic testing (procedure codes) among carriers; (3) adjusted odds of documented testing among women, by ancestry, before and after socioeconomic adjustment, using multivariable logistic regression. Results: Among 414,830 participants, P/LP HBOC carrier prevalence was 1.42% (95% CI, 1.38-1.45) overall and similar across ancestry groups (AFR 1.24%, AMR 1.32%, EAS 1.19%, EUR 1.52%, MID 1.68%, SAS 1.33%; overlapping CIs). Among 250,071 women in the testing analysis, documented clinical genetic testing was rare: only 74 of 5,878 carriers overall (1.3%) and 59 of 3,572 European-ancestry carriers (1.7%) had a documented test, with counts below reportable thresholds in all other ancestry groups. African-ancestry women had lower adjusted odds of documented testing than European-ancestry women (Model 1 adjusted odds ratio [aOR], 0.32; 95% CI, 0.27-0.39), an association that attenuated but persisted after adjustment for income and education (Model 2 aOR, 0.48; 95% CI, 0.40-0.58; P < 0.001); Admixed American women also had reduced adjusted odds (aOR, 0.71; 95% CI, 0.61-0.84). Lower income and lower education were independently and dose-dependently associated with lower testing odds (income <$25,000 aOR, 0.46; high-school education aOR, 0.54). Conclusions and Relevance: High-risk HBOC variant carriers are present across all ancestry groups at similar frequencies, yet documented clinical genetic testing was disparate in the different ancestry groups. African-ancestry women experience a testing gap that is not fully explained by socioeconomic position, implicating structural barriers in access and referral. Population-level strategies that decouple carrier identification from current referral pathways may be required to close this gap.

Importance: Abdominal pain causes roughly 10 million US emergency department (ED) visits annually, most resulting in discharge. Post-discharge courses vary, yet existing risk models predict only whether an ED revisit occurs, not what that revisit outcome will entail. Objective: To evaluate whether Curiosity, a generative medical event foundation model, can predict post-ED-discharge trajectories for adults with abdominal pain, differentiating the timing and severity of expected outcomes. Design: Retrospective cohort study; encounters January 1-December 31, 2022; 30-day follow-up; analysis conducted in 2026. Setting: Epic Cosmos research network (multicenter, population-based, de-identified electronic health record). Participants: Adults ([&ge;]18 years) discharged from the ED with abdominal pain, excluding training-set patients. Random sample of 3,000 drawn from 150,030 eligible patients (65.3% female; median age 47 years [IQR 36-60]). Exposure: ED discharge after evaluation for abdominal pain. Main Outcomes and Measures: Primary: Curiosity model vs. per-task, separately estimated XGBoost models on area under the receiver operating characteristic curve (AUROC) for ED revisit ending in admission (admit-revisit), ED revisit ending in discharge (DC-revisit), and any ED revisit at 72 hours, 7 days, and 30 days. Secondary: trajectory-level accuracy across 36 trajectory classes and edit distance vs XGBoost; calibration of simulated vs observed conditional path probabilities across 45 transitions. Results: Curiosity identified patients at high risk of revisit requiring admission more accurately than XGBoost and differentiated those likely to revisit without admission. Among 3,000 patients, Curiosity's 30-day admit-revisit AUROC was 0.83 (95% CI 0.79-0.87) vs 0.70 (95% CI 0.65-0.75) for XGBoost (DeLong P<.001), and admit-revisit AUC-PR was 0.37 (95% CI 0.29-0.46) against a 4.1% cohort base rate, vs XGBoost 0.13 (95% CI 0.09-0.19). Curiosity identified the most likely trajectory out of 36 possibilities for 45.9% of patients (XGBoost 41.0%; McNemar P<.001), with median edit distance 1.28 vs 1.40 (Wilcoxon P<.001). Median absolute calibration error across 45 transitions was 1.30 percentage points (95% CI 0.32-2.49). Conclusions and Relevance: A generative medical event foundation model produced calibrated trajectory-level predictions and discriminated admit-revisits more effectively than task-specific XGBoost baselines, separating patients that revisited and were admitted from those who revisited and were discharged.