JAMA Network Open

BackgroundIntentional inhalation of 1,1-difluoroethane (DFE), the propellant in aerosol duster products, is a leading cause of inhalant-abuse death in the United States. The CPSC has cited death counts from its Clearinghouse in regulatory proceedings, yet no peer-reviewed publication has described the methods used to identify these cases. ObjectivesTo estimate DFE- and duster-related deaths in the CPSC Clearinghouse for 2011-2021, characterize reporting patterns, and assess classification reliability against an independently coded dataset. MethodsDeath records (N = 6,316) were identified from 261,076 Clearinghouse records using CPSC product codes for chemicals, aerosols, gases, and related products. Each record was classified through narrative review and substance coding. Inter-rater reliability was assessed against an independently coded dataset from Families United Against Inhalant Abuse (FUAIA) using Cohens kappa and Gwets AC1. ResultsOf 2,451 inhalant-abuse deaths identified (70.8% male; mean age 36.9 years), 2,097 (85.6%) involved DFE or aerosol duster products. DFE/duster deaths rose from 110 (2011) to 266 (2016). Only 17% of cases were received in the same calendar year as the incident. Prior to reconciliation, comparison with the FUAIA dataset yielded Cohens kappa of 0.90 (95% CI [0.89, 0.91]); all discrepancies were subsequently resolved through joint review. ConclusionAerosol duster products account for approximately 86% of inhalant-abuse deaths reported to the CPSC Clearinghouse; however, these counts significantly underestimate true prevalence. The concentration of mortality in a single, widely available product class supports targeted product-level interventions and provides the first peer-reviewed baseline for evaluating the impact of regulatory and prevention efforts.

Key PointsO_LIIn a randomized trial, an educational booklet and video did not increase evaluation completion or living donor kidney transplant receipt. C_LIO_LIFor patients who received the booklet and video intervention, experiencing discrimination in healthcare reduced evaluation completion. C_LIO_LILong-term follow-up and a large sample size yielded sufficient power to validate a true null effect of the intervention on key outcomes. C_LI BackgroundKidney transplantation (KT) evaluation is a complex, lengthy process; and living donor KT (LDKT) is the optimal treatment for kidney failure. Interventions at the start of evaluation may improve evaluation completion and LDKT rates. This study tested whether (a) an educational booklet and video (the "Talking About Living Kidney donation" [TALK] intervention) increased evaluation completion and LDKT when delivered under a streamlined KT evaluation program; and (b) if no effects found, explore differential effects by psychosocial/sociocultural factors (e.g., healthcare-related discrimination). MethodsWe conducted a randomized-controlled trial of the TALK intervention using permuted block randomization at an urban transplant center. Participants were enrolled 05/2015-06/2018; follow-up through 08/2022. Staff were blinded to block size, not allocation. Fine-Gray proportional hazards models examined intent-to-treat and per-protocol approaches. Primary outcomes were the cumulative incidence of evaluation completion and LDKT receipt. We explored interaction analyses by psychosocial/sociocultural factors and TALK-assignment. ResultsAmong 1108 participants (574 [52%] TALK, 534 [48%] No-TALK; median age: 59.13 [IQR: 48.92-67.10]; 243 [22%] Black, 783 [71%] White, 82 [7%] Other; 695 [63%] male), TALK did not significantly improve evaluation completion (sub-distribution hazard [SHR]=1.06; 95% CI: 0.92-1.22) or LDKT receipt (SHR=0.83; 95% CI: 0.55-1.25) in intent-to-treat and per-protocol analyses. In exploratory per-protocol analyses, discrimination significantly modified the effect of TALK on evaluation completion (SHR=0.42; 95% CI: 0.29-0.61). The "No-Discrimination" TALK participants had greater evaluation completion than No-TALK (SHR=1.32; 95% CI: 1.10-1.58), but the "Discrimination" TALK participants had lower evaluation completion than No-TALK (SHR=0.56; 95% CI: 0.41-0.77). ConclusionsDespite streamlined care, TALK did not improve evaluation completion or LDKT rates. A significant interaction in the per-protocol analyses for evaluation completion suggests prior healthcare-related discrimination may limit educational intervention effectiveness. Future studies should explore approaches that address systemic barriers and complement, rather than rely on, educational strategies to promote LDKT (ClinicalTrials.gov Identifier: NCT02342119).

AimsTo characterize decreases in overdose mortality in the United States between 2023 and 2024 by substance involvement, geography, race/ethnicity, demographic, and other key dimensions. DesignPopulation-based study of national death records. SettingUnited States. Participants/casesAll individuals who died from drug overdose between January 1999 and December 2024. MeasurementsAnnual or monthly (annualized) overdose deaths per 100,000 population. Year and month of occurrence of overdose death; substance involvement; census region and division; state; county; race/ethnicity, age, and sex. FindingsAfter over two decades of mostly exponential increases, monthly data show consistent decreases in overdose deaths between June 2023 and December 2024. Decreases reflected declining illicit fentanyl-involved deaths (with and without stimulants); however, increasing trends through 2024 were still seen in deaths involving stimulants without fentanyl, and those involving xylazine. Death rates in the Northeast, South and Midwest fell to 19.5, 19.4 and 17.3 per 100,000, respectively, in December 2024, but remained elevated in the West, compared with other regions, at 27.2 per 100,000. Non-Hispanic Black and African Americans had the largest decrease in death rates in 2023-2024 falling 29.3%, but remained elevated at 36.0 per 100,000, compared to the national average of 23.7 per 100,000. Non-Hispanic American Indian and Alaska Native individuals had the highest overdose mortality rate in 2024, at 50.8 per 100,000. ConclusionsRecent decreases in overdose deaths are encouraging and unprecedented. Racial gaps remained large but shrunk by a modest margin. The geography of the overdose crisis has shifted, with the West now the most affected region, which may have implications for the targeting of funding. The nature of the crisis is also shifting, as stimulants and xylazine continue to represent increasingly important public health challenges, and renewed attention to nonfatal aspects of addiction in the US is needed.

IntroductionAlthough prior studies suggest e-cigarette use is associated with worse mental health, it remains unclear whether these associations persist independent of diagnosed depression and how tobacco use and depression jointly affect health-related quality of life. Although the long-term health risks of vaping are still unknown, self-reported health is a reliable measure of quality of life. This study provides the first health utility estimates of the independent and combined effects of cigarette use, e-cigarette use, and depression on health-related quality of life. MethodsWe analyzed 2022-2023 Behavioral Risk Factor Surveillance System data on health-related quality of life as measured by self-reported physically or mentally unhealthy days in the past 30 days. The average number of unhealthy days was estimated by age, gender, smoking status (current versus non-smoking), depression status (received a prior diagnosis), and current e-cigarette use status (every day or some day use). We converted the number of overall healthy days into EQ-5D utility scores by age-specific percentile matching of BRFSS and MEPS distributions, a method developed by Jia and Lubetkin. ResultsCigarette use, e-cigarette use, and depression were each associated with worse health-related quality of life. Mentally unhealthy days increased with the accumulation of these conditions. Associations with physically unhealthy days followed a similar pattern, particularly among younger adults, although the magnitude of association was smaller. E-cigarette use alone was associated with 2.0-4.2 (95% CI: 2.0-4.6) additional mentally unhealthy days per month across all age groups. Notably, e-cigarette use was independently associated with poorer mental health among adults aged 18-64 with or without diagnosed depression. After accounting for smoking and depression status, e-cigarette use was associated with disutility scores of 0.011 in men and 0.015 in women among young adults, with the largest losses observed when multiple conditions co-occurred. ConclusionE-cigarette use is associated with poorer health-related quality of life, particularly among younger adults, and these effects are amplified when combined with cigarette use and depression. Quantifying these joint impacts as health utility losses highlights the importance of addressing e-cigarette use within integrated tobacco control and mental health policies, especially for young populations.

Background: Early initiation of substance use is linked to later adverse outcomes, and risk factors come from multiple domains and are shared across substances. In our previous work, traditional time-to-event Cox models identified individual risk factors, but these models are not designed to jointly model multiple outcomes or capture complex non-linear relationships. Multi-task learning (MTL) can leverage shared structure across related outcomes to improve prediction and distinguish common versus substance-specific predictors. However, most MTL studies rely on baseline features and focus on single outcomes, which limits their ability to capture shared risk and temporal changes. Substance use initiation is a time-dependent process that unfolds during development and reflects changing exposures over time. Baseline-only models cannot capture these changes or represent risk dynamics. Discrete-time modeling provides a practical approach by estimating interval-level initiation risk and combining it into cumulative risk at the subject level. By integrating multi-task learning with dynamic modeling, it is possible to share information across outcomes while capturing how risk evolves over time, which may improve prediction performance. Methods: Using the Adolescent Brain Cognitive Development (ABCD) Study (release 5.1), we developed two complementary multi-task learning (MTL) frameworks to predict initiation of alcohol, nicotine, cannabis, and any substance use. A baseline MTL model predicted fixed- horizon (48-month) initiation using one record per participant, while a dynamic discrete-time MTL model incorporated longitudinal interval data to model time-varying risk. Both models used multi-domain environmental exposures, core covariates, and polygenic risk scores (PRS). Performance was evaluated on a held-out test set using AUROC, PR-AUC, and calibration metrics, and compared with single-task logistic regression (LR). Feature importance was assessed using permutation importance and compared with Cox proportional hazards models. Results: MTL showed comparable or improved performance relative to LR, with larger gains for low-prevalence outcomes (cannabis and nicotine). Incorporating longitudinal information led to consistent improvements across all outcomes. Dynamic models increased AUROC by +0.044 to +0.062 for MTL and +0.050 to +0.084 for LR, indicating that temporal information was the primary driver of performance gains. Feature importance analyses showed modest overlap across methods, with higher agreement between dynamic MTL and Cox models than static MTL. A small set of features, including externalizing behavior, parental monitoring, and developmental factors, were consistently identified across all approaches. Conclusions: Dynamic multi-task learning improves the prediction of substance use initiation by leveraging longitudinal structure and shared information across outcomes. While MTL provides additional gains, incorporating time-varying information is the dominant factor for improving performance. Combining baseline and dynamic frameworks offers a comprehensive strategy for identifying robust risk factors and modeling adolescent substance use initiation.

ImportanceDrug-related pregnancy-associated mortality is a leading contributor to the US maternal mortality crisis, yet whether it follows the persistent rural disadvantage documented for all-cause maternal mortality--or is restructured by the geographic dynamics of drug markets--has not been established. ObjectiveTo characterize geographic variation in pregnancy-associated overdose (OD) and substance use disorder (SUD) mortality across the rural-urban continuum and by US Census region from 2016 through 2022. Design, Setting, and ParticipantsNational population-based surveillance study using individual-level National Vital Statistics System (NVSS) mortality and natality records. Pregnancy-associated deaths (occurring during pregnancy or within one year of the end of pregnancy) were ascertained among 25,007,723 live births during 2016-2022 using the NVSS 2018 algorithm. ExposuresRural-urban classification cross-classified by four US Census regions. Main Outcomes and MeasuresRates of pregnancy-associated OD mortality and SUD mortality per 100,000 live births. Post-COVID excess OD mortality was estimated using a Bayesian hierarchical Poisson model. ResultsThere were 516 OD deaths (2.06 per 100,000 live births) and 1,080 SUD deaths (4.32 per 100,000) nationally; SUD exceeded OD mortality more than two-to-one in all strata, and both outcomes were concentrated in the late postpartum period (43 days to 1 year). OD mortality converged across the rural-urban gradient during the COVID era (2020-2022)--the inverse of the persistent rural disadvantage in all-cause maternal mortality--with metropolitan areas falling below pre-pandemic trajectory expectations while non-metropolitan areas exceeded theirs. Credible excess OD mortality was identified in non-metropolitan Southern and Northeastern counties. SUD rates were non-monotonic across urbanicity, with metro-adjacent counties carrying elevated rates in all regions. Conclusions and RelevanceDrug-related pregnancy-associated mortality follows a distinct geographic logic from all-cause maternal mortality, shaped by drug supply dynamics and harm reduction geography rather than obstetric care infrastructure alone. The convergence of OD mortality across the rural-urban gradient, the dominance of SUD over acute overdose, and the concentration of deaths in the late postpartum year point to care and surveillance gaps requiring integrated obstetric and addiction treatment, extended postpartum insurance coverage, and rural harm reduction capacity.

BackgroundAs cigarette smoking continues declining among youth and young adults, smoking rates among older Americans remain unchanged. Historically, cigarette and, more recently, e-cigarette tax policies influenced smoking behavior in younger smokers. Understanding how older smokers respond to tax changes can inform public health strategies. MethodsWe assembled a quarterly panel of state cigarette and e-cigarette tax rates using the CDC STATE System Tobacco (2000-2024) and E-Cigarette Legislation databases (2015-2024), then merged these data to individual-level survey responses from the Behavioral Risk Factor Surveillance System (BRFSS). Our sample included all adults aged [≥]65, but our main specification included adults with a history of smoking. We estimated two-way fixed-effects population-weighted linear probability regression models of current smoking and past-year quit attempts. ResultsAmong 3,117,382 adults, 50% had a history of smoking; from which 18% currently smoked. A one-dollar tax increase was associated with current smoking: cigarette tax = -0.61-percentage points (CI = -0.94,-0.28); e-cigarette tax = +0.19-percentage-points (CI = 0.14,0.24). There was no association between cigarette taxes and quit attempts. A one-dollar increase in e-cigarette tax was associated with reduced quit attempts (-0.17-percentage-points; CI = -28,-0.06). Approaching tax parity by one dollar was associated with increased current smoking by 0.23-percentage-points (CI = 0.16,0.29) and reduced quit attempts by -0.17-percentage-points (CI = -0.29,0.05). ConclusionsOlder American smokers appear responsive to cigarette and e-cig tax changes. Policies increasing the relative cost of e-cigarettes may impede cessation and perpetuate smoking rates in older generations at the highest risk of smoking related harm. ImplicationsMany studies have investigated the impact of cigarette and e-cigarette tax changes on smoking behavior in youth or younger adults. This study adds new evidence quantifying how new cigarette and e-cigarette taxes change smoking behavior among older adults, a population yet to realize reductions in smoking despite higher risk of tobacco related harm. Analyzing large, population-based survey data, we show that older smokers change smoking behavior due to cigarette and e-cigarette taxes. Taxation of e-cig products may produce unintended harm among older smokers. Specifically, approaching tax parity may increase cigarette smoking and reduce quit attempts among older adults with a history of smoking.

Importance: Parent/caregiver-infant early relational health (ERH) is known to play a critical role in the promotion of socio-emotional functioning and wellbeing across the life course. The negative impact of the COVID-19 pandemic on maternal mental health and secondarily on ERH and child socio-emotional functioning is clear. However, the direct impact of maternal viral exposure during pregnancy on ERH has not been investigated. Objective: The goal of this study was to determine the impact of prenatal SARS-CoV-2 exposure on ERH and infant socio-emotional functioning in the first 6 months of life. Design: Mothers with and without SARS-CoV-2 exposure during pregnancy who gave birth from 02/2020 to 09/2021 were enrolled from 05/2020 to 09/2021 in one of two parallel prospective studies (the COVID-19 Mother Baby Outcomes [COMBO] Initiative or the Respiratory Syndrome Coronavirus 2 in Pregnancy and Infancy [ESPI] COMBO sub-study). Mothers reported on their health and the socio-emotional functioning of their infant via online surveys (REDCap) at enrollment, 1, 2, 4, and 6 months. At 4 to 6 months, dyads were invited to participate in a video-based, remote assessment of ERH. Participants: 884 mother-infant dyads from three U.S. States (Alabama, New York, and Utah). Exposure: Prenatal SARS-CoV-2. Main Outcomes and Measures: Maternal-reported ERH (parental stress, parenting confidence and bonding) and observer-based ERH (video-coded quality of maternal caregiving behaviors and mother-infant emotional connection). Infant socio-emotional development assessed using the 6-month Ages and Stages Questionnaire: Socio-Emotional 2nd Edition (ASQ:SE-2). Results: 316 (36%) mothers had a positive prenatal SARS-CoV-2 exposure. Prenatal SARS-CoV-2 exposure was associated with an adjusted estimate of ~5% reduction (incidence rate ratio=0.95, 95% confidence interval [0.90, 1.00], p=0.03) in observed maternal caregiving quality, after accounting for postnatal maternal mental health and sociodemographic factors. We found no evidence of effect on other ERH constructs or infant socio-emotional functioning. Conclusions and Relevance: In this large prospective cohort study, prenatal SARS-CoV-2 was associated with a small decrement in caregiving quality, but not other ERH constructs or infant socio-emotional functioning. These findings should be interpreted as hypothesis generating and will require replication in independent studies.

ImportancePersistent racial and ethnic disparities in breast and prostate cancer mortality are well documented. Most prior studies emphasize between-group differences and rely on population averages or single composite measures of social disadvantage, which can obscure high-need communities within groups. How socio-behavioral determinants of health vary within groups across local gradients of cancer mortality remains incompletely characterized. A framework that combines race- and cancer-specific mortality with local, domain-level socio-behavioral profiles may help identify where burden is greatest and which specific barriers warrant prioritization. ObjectiveTo determine how socio-behavioral risk relates to breast and prostate cancer mortality within racial and ethnic groups and to characterize domain-specific behavioral profiles across low-, moderate- and high-mortality counties to inform targeted, equity-oriented cancer control strategies. DesignCross-sectional study of U.S. counties. Setting United States, county-level analysis. Participants3,141 U.S. counties, stratified within Non-Hispanic White, Non-Hispanic Black, and Hispanic populations. ExposuresCounty-level socio-behavioral determinants of health measured using a composite index comprising seven domains: community solidarity; education, health literacy, and digital connectivity; quality of care; housing and environmental risk; economic livelihoods; lifestyle behaviors; and touchpoints with care. Main outcomes and measuresRace/ethnicity-specific, age-adjusted breast and prostate cancer mortality rates (2018-2022) and county-level socio-behavioral risk scores. Counties were grouped into mortality tertiles within each race/ethnicity-by-cancer-stratum. ResultsAcross groups, higher socio-behavioral risk was associated with higher breast and prostate cancer mortality. For breast cancer, socio-behavioral risk increased monotonically across mortality tertiles for all groups, with the largest within-group increases among Hispanic and Non-Hispanic Black women. For prostate cancer, risk generally increased across mortality tertiles for all groups. Although Hispanic populations had lower population-average mortality, high-mortality Hispanic counties exhibited pronounced risk in lifestyle behaviors, economic livelihoods, and touchpoints with care. Domain patterns associated with high mortality varied by race, ethnicity, and cancer type, with touchpoints with care and economic livelihoods consistently prominent. Conclusions and relevanceWithin-group heterogeneity in socio-behavioral risk is substantial across U.S. counties. Linking population-specific, domain-level socio-behavioral profiles to cancer mortality may support more precise and equity-oriented cancer control strategies than reliance on group averages or composite indices. Key pointsO_ST_ABSQuestionC_ST_ABSWithin racial and ethnic groups, how do socio-behavioral determinants of health vary across US counties with low, moderate, and high breast and prostate cancer mortality? FindingsIn this cross-sectional study, higher county-level socio-behavioral risk was associated with higher breast and prostate cancer mortality across racial and ethnic groups. Race/ethnicity-specific, domain-level profiles revealed within-group heterogeneity, including persistently elevated risk among Non-Hispanic Black populations and pronounced domain-specific gaps in high-mortality Hispanic counties. MeaningLinking population-specific socio-behavioral profiles to local cancer mortality can guide more precise and equity-oriented prioritization of intervention domains and geographies than reliance on group averages or composite indices.

ObjectiveAlthough trauma-focused psychotherapies are effective for posttraumatic stress disorder (PTSD), recovery under routine outpatient conditions remains variable. We examined whether participation in a structured Specialty Care (SC) model integrating clinician specialization, flexible treatment density, and coordinated navigation was associated with accelerated PTSD recovery compared with standard outpatient care. MethodsWe conducted a retrospective matched cohort study (2024-2025) of U.S. adults with active PTSD symptoms (PTSD Checklist for DSM-5 score [&ge;]31) receiving care through an employer-sponsored digital mental health platform. Access to SC was determined by employer benefit design. Propensity-score matching with weighting balanced cohorts on baseline severity and demographics. Primary outcomes included longitudinal PTSD symptom trajectories and time to recovery, remission, and reliable improvement. Secondary outcomes assessed depressive symptoms (PHQ-9). Linear mixed-effects and Cox proportional hazards models were applied. ResultsThe matched sample included 356 SC and 9,409 standard care participants. SC participants received higher treatment intensity, including greater session volume and faster early follow-up, and greater care navigation engagement. SC participation was associated with steeper PTSD symptom decline ({beta} = -1.3 per log-week, p < .001) and a higher likelihood of recovery (hazard ratio = 1.31; 95% CI, 1.10-1.57). At 12 weeks, predicted recovery was 29% in SC versus 23% in standard care. Depressive symptoms improved in both groups, without significant differences in time to categorical recovery. ConclusionsUnder routine outpatient conditions, a structured SC model was associated with accelerated PTSD recovery, suggesting that reorganization of outpatient delivery may improve real-world outcomes.

ImportanceDonor hospitals in the United States are assigned to a designated organ procurement organization (OPO) responsible for managing deceased donors in the designated donation service area (DSA). Donor hospitals can apply for waivers to work with a different OPO with appropriate justification, and beginning with the 2026 OPO certification cycle, the highest-performing OPOs can bid to work with donor hospitals managed by intermediate- and low-performing OPOs. ObjectiveWe sought to evaluate the impact of donor hospital waivers on organ donation activity. DesignRetrospective cohort study. SettingWe evaluated Organ Procurement and Transplantation Network (OPTN) data from two OPOs (Donor Network West and Honor Bridge), each with a donor hospital (Renown Regional Medical Center and North Carolina Baptist Hospital) in its DSA granted a waiver to work with a different OPO beginning in April 2025. Main OutcomeWe assessed changes in the number of organ donors and organs transplanted pre- and post-granting of a waiver using a difference-in-differences approach based on multilevel mixed-effects models. ResultsAfter switching OPO affiliations, these two donor hospitals had marked and statistically significant increases in the number of donors recovered and organs transplanted, despite stable numbers of reported deaths at each hospital. In multivariable models, switching OPO affiliations was associated with a statistically significant increase in donors recovered and organs transplanted. Conclusion: With eight months of post-waiver data, donor hospitals with granted waivers had significant increases in donation activity driven by improved donor conversion rather than changes in referral patterns or organ yield per donor. Although longer-term data are needed to confirm these findings, CMS and the organ transplant community should feel confident that changing donor hospital-OPO affiliations will not negatively impact donation and may lead to significant increases in donation. These data also counter unfounded concerns that the continued granting of waivers and realignments of donor hospital-OPO affiliations during the 2026 recertification cycle will lead to a collapse of the system of organ donation. KEY POINTSO_ST_ABSQuestionC_ST_ABSDo donor hospitals who request a waiver to change OPO affiliations have changes in organ donation rates? FindingsUsing a difference-in-difference approach, the two donor hospitals who changed OPO affiliations had a significant increase in organ donors and organs transplanted after being granted a waiver. MeaningDonor hospitals that change OPO affiliations have an immediate increase in organ donation activity.

Introduction Cesarean delivery accounts for nearly one-third of U.S. births and is associated with substantial maternal morbidity and health care costs. Persistent racial disparities have been documented, yet the structural factors contributing to these differences remain incompletely understood. The extent to which insurance coverage shapes racial disparities in cesarean delivery remains unclear. Objective To evaluate the independent and interactive associations of race/ethnicity and insurance coverage with cesarean delivery in the United States. Methods Population-based retrospective cohort study using singleton live births recorded in the United States Vital Statistics Natality files from 2014 to 2024. Multivariable logistic regression was used to estimate the independent effects of race/ethnicity and insurance status on cesarean delivery, including interaction terms to test effect modification, using national birth certificate data. Models were adjusted for maternal demographics, clinical factors, and temporal covariates. Adjusted odds ratios, predicted probabilities, and absolute risk differences were derived from post-estimation marginal effects. The main outcome measure was cesarean delivery (yes vs no). Results Among 41,543,568 deliveries from 2014 to 2024, 13,312,221 (32.0%) were cesarean deliveries. After adjustment, both race and ethnicity and insurance status were independently associated with cesarean delivery. Compared with non-Hispanic White women, non-Hispanic Black women had higher odds of cesarean delivery (odds ratio [OR], 1.22; 95% CI, 1.22-1.23). Relative to uninsured women, those with private insurance had 59% higher odds of cesarean delivery (OR, 1.59; 95% CI, 1.58-1.60). Significant interaction effects were observed, indicating that insurance coverage modified racial and ethnic differences in cesarean delivery. Non-Hispanic Black women had the highest predicted probabilities across all insurance categories, with the largest absolute disparities observed among uninsured women. Conclusion Racial and ethnic differences in cesarean delivery persist in the United States and are modified by insurance coverage, suggesting that coverage-related differences may contribute to inequities in obstetric care.

Pregnant and postpartum people who use drugs in the United States are trying to survive at the intersection of two of the gravest public health crises of the 21st century US: epidemics of (1) maternal mortality and (2) the overdose epidemic. Although extensive evidence documents racial/ethnic disparities in each of these epidemics separately, comparatively little research has characterized racial/ethnic patterns in their collision, that is, in maternal overdose mortality. We analyzed individual-level mortality records from the National Vital Statistics System (NVSS) for 2016-2022 to describe racial/ethnic disparities in pregnancy-associated overdose deaths (PA-OD) and pregnancy-associated substance use disorder-related deaths (PA-SUD). Racial/ethnic-specific mortality rates were calculated per 100,000 live births with exact Poisson confidence intervals. Temporal trends were summarized using annual percent change (APC), and disparities were quantified using rate ratios and differences relative to non-Hispanic White individuals. Overdose-related maternal mortality increased substantially during the study period across multiple racial and ethnic groups. Rates increased nearly threefold among non-Hispanic White individuals and rose more steeply among non-Hispanic Black individuals, producing a Black-White disparity that emerged over time. Rates among Hispanic individuals remained lower but increased rapidly, while estimates among American Indian and Alaska Native individuals were often high but unstable because of small counts. Substance use disorder-related maternal mortality exhibited a pronounced surge during 2019-2021 across several racial and ethnic groups. These findings highlight rapidly evolving racial/ethnic patterns in maternal overdose mortality and underscore the need for targeted prevention and harm-reduction strategies to reduce overdose-related deaths during pregnancy and the postpartum period. FundingWe are grateful to the following NIH grants for supporting this research: U54HD113292 and R01DA059182.

Introduction: COVID-19 transmission has not been evenly distributed across racial groups, with exposure being shaped by social and structural factors. The emergence of highly transmissible variants (i.e., Omicron) dramatically increased infection rates. However, it remains unclear whether racial disparities in transmission disappeared or persisted over the course of the pandemic. Objective: To understand how SARS-CoV-2 transmission differed by race in Canada and whether those disparities changed with the Omicron variant. Methods: We analyzed cross-sectional SARS-CoV-2 seroprevalence data from the Canadian Blood Services serosurveillance program (June 2020 to April 2023) using a previously described dynamic susceptible-infection model, while accounting for seroreversion. Race-specific force of infection was estimated for the pre-Omicron and Omicron periods (with the emergence of Omicron defined as beginning December 26, 2021). Results: Prior to Omicron, racialized individuals had a 74% higher force of infection (IRR = 2.205; 95% CI: 2.115-2.299). During the Omicron period, infection rates rose significantly within each racial group relative to the pre-Omicron period, with a 55.52-fold increase among White individuals and a 31.27-fold increase among racialized individuals. Despite this, racialized individuals remained disproportionately affected following the emergence of Omicron, with 24% higher infection rates than those of their White counterparts (IRR = 1.242; 95% CI: 1.231-1.253). Conclusion: Widespread transmission during Omicron did not result in epidemiologic equity, as racialized populations continued to experience higher infection risk despite crude seroprevalence depicting convergence.

ImportanceGuideline-concordant care for young children with attention-deficit/hyperactivity disorder (ADHD) includes recommending parent training in behavior management (PTBM) as first-line treatment. However, assessing guideline adherence through manual chart review is time-consuming and costly, limiting scalable and timely quality-of-care measurement. ObjectiveTo evaluate the accuracy and explainability of large language models (LLMs) in identifying PTBM recommendations in pediatric electronic health record (EHR) notes as a scalable alternative to manual chart review. Design, Setting, and ParticipantsThis retrospective cohort study was conducted in a community-based pediatric healthcare network in California consisting of 27 primary care clinics. The study cohort included children aged 4-6 years with [&ge;] 2 primary care visits between 2020-2024 and ICD-10 diagnoses of ADHD or ADHD symptoms (n=542 patients). Clinical notes from the first ADHD-related visit were included. A stratified subset of 122 notes, including all cases with model disagreement, was manually annotated to assess model performance in identifying PTBM recommendations and rank model explanations. ExposuresAssessment and plan sections of clinical notes were analyzed using three generative large language models (Claude-3.5, GPT-4o, and LLaMA-3.3-70B) to identify the presence of PTBM recommendations and generate explanatory rationales and documentation evidence. Main Outcomes and MeasuresModel performance in identifying PTBM recommendations (measured by sensitivity, positive predictive value (PPV), and F1-score) and qualitative explainability ratings of model-generated rationales (based on the QUEST framework). ResultsAll three models demonstrated high performance compared to expert chart review. Claude-3.5 showed balanced performance (sensitivity=0.89, PPV=0.95, and F1-score=0.92) and ranked highest in explainability. LLaMA3.3-70B achieved sensitivity=0.91, PPV=0.89, and F1-score=0.90, ranking second for explainability. GPT-4o had the highest PPV [0.97] but lowest sensitivity [0.82], with an F1-score of 0.89 and the lowest explainability ranking. Based on classifications from the best-performing model, Claude-3.5, 26.4% (143/542) of patients had documented PTBM recommendations at their first ADHD-related visit. Conclusions and RelevanceLLMs can accurately extract guideline-concordant clinician recommendations for non-pharmacological ADHD treatment from unstructured clinical notes while providing clear explanations and supporting evidence. Evaluating model explainability as part of LLM implementation for medical chart review tasks can promote transparent and scalable solutions for quality-of-care measurement.

BackgroundArtificial intelligence and machine learning (AI/ML) are among the fastest-growing domains in NIH research funding, but whether children have shared equitably in this expansion is unknown. We characterized pediatric representation in NIH AI/ML funding from fiscal years (FY) 2020 to 2024. MethodsNIH grant data were obtained from Research Portfolio Online Reporting Tools Expenditures and Results bulk files for FY2020 to FY2024. AI/ML grants were identified using the NIH Research, Condition, and Disease Categorization "Machine Learning and Artificial Intelligence" category, and pediatric grants using the "Pediatric" category. Subprojects were excluded. Grants were deduplicated within each fiscal year by core project number for trend analyses and across all years retaining the most recent fiscal year for cross-sectional totals. Disease areas were identified by keyword searches of titles and abstracts. ResultsAcross FY2020 to FY2024, 5,624 unique NIH AI/ML grants totaling $3,371 million were identified. Of these, 836 grants (14.9%) were classified as pediatric, representing $401 million (11.9%) of total NIH AI/ML funding. Although this share was consistent with the historically reported overall NIH pediatric funding baseline of approximately 10% to 12%, it remained substantially below the US pediatric population share of approximately 22%. The pediatric share of NIH AI/ML funding declined from 12.3% in FY2020 to 10.8% in FY2024, despite growth in absolute pediatric funding. Indexed to FY2020, pediatric AI/ML funding grew approximately 2.6-fold compared with 3.0-fold growth in the total portfolio. Across disease areas, unadjusted adult/general-to-pediatric funding ratios ranged from 2.0-fold in mental health to 9.8-fold in cancer. ConclusionsPediatric representation in NIH AI/ML funding remained low and declined over time as the overall portfolio expanded. These findings suggest that growth in NIH AI/ML investment has not been matched by proportional gains for pediatric research.

Background Six years after its emergence, SARS-CoV-2 continues to have a substantial burden. The impact of vaccination and the optimal timing of its rollout remain uncertain given existing population immunity and variability in outbreak timing between summer and winter. Methods The US Scenario Modeling Hub convened its 19th round of ensemble projections for COVID-19 hospitalizations and deaths in the United States, where eight teams projected trajectories in each US state and nationally from April 2025 to April 2026 under five scenarios regarding vaccine recommendations and timing. Recommendations had two eligibility scenarios (high-risk individuals only and all-eligible) and two timing scenarios (classic start: mid-August, earlier start: late June). These were crossed to create four scenarios and were compared against a counterfactual scenario with no vaccination. Findings Compared to no vaccination, our ensemble projections estimated 90,000 (95% PI 53,000-126,000) hospitalizations averted in the high-risk and classic timing scenario across the US. Expanding to all-eligible age-groups averted an additional 26,000 (95% PI 14,000-39,000) hospitalizations, which when coupled with the early vaccination timing, was projected to further reduce national hospitalizations by 15,000 (95% PI -3,000-33,000). The majority of teams projected both summer and winter waves. Implications We project COVID-19 will cause significant hospitalizations and deaths in the US in the 2025-26 season and estimate significant benefits from a broad all-eligible vaccination recommendation. The results also suggest an additional benefit is likely to be gained from an earlier vaccination campaign. Funding Centers for Disease Control and Prevention; National Institute of Health (US), National Science Foundation (US)

BackgroundSkin disease affects 4.7-4.9 billion individuals globally; however, little is known about access to dermatological care. MethodsWe conducted a multinational, cross-sectional survey of dermatological care across 194 WHO member states and three additional geographic areas in 2024-2025. Primary outcomes included dermatologist density per 100,000 population and number of dermatologists globally. Secondary outcomes included training programme density, workforce distribution, perceived access to care, and health system characteristics. Descriptive statistics and nonparametric tests compared outcomes across World Bank Income (WBI) levels and WHO regions. FindingsResponses were obtained from 158 countries. Mean dermatologist density was 2.66 per 100,000, ranging from 0.37 in low-income (LICs) to 5.05 in high-income countries (HICs). There are estimated 175,633 dermatologists globally (95% CI: 173,598-177,668). Forty-two percent of countries reported inadequate or extremely poor access to dermatological care. There was significant variation (p < 0.001) in access to all types of subspecialty care (paediatric, surgical, dermatopathology) across WBI levels, with consistently worse access in lower-income countries. Dermatologists are primarily based in urban centres (79%). Twenty-one percent of countries lack dermatology training programs, with training varying by WBI level (p < 0.001). Non-dermatologist healthcare workers bear a substantial responsibility for management of skin disease. InterpretationSignificant global disparities exist in access to dermatological care, particularly in lower resource settings. Achieving skin health equity will require global commitment to expanding/funding training programmes, incentivizing decentralization of dermatology practice, and optimizing alternative care delivery including upskilling front-line healthcare workers. FundingInternational League of Dermatological Societies and LOreal Dermatological Beauty.

ObjectivesUptake of evidence-based smoking cessation support remains limited. Digital interventions offer the prospect of scalable and highly accessible support. Smoke Free, a digital mobile application using established behaviour change techniques, has shown promise, but no large-scale randomised controlled efficacy trial has yet been conducted. We assessed its effectiveness for smoking cessation. DesignIn this prospective, randomised, controlled, two-arm, parallel clinical trial with 6-month follow-up, study personnel and patients were blinded. SettingThe trial was conducted nationwide in Germany, utilising a decentralised, fully remote trial design. Enrolment took place digitally after receiving brief advice from a healthcare professional, following guidelines for primary care. ParticipantsOut of a volunteer sample of 1850 patients assessed for eligibility, 1466 adult cigarette smokers who had at least moderate cigarette dependence (F17.2, FTCD[&ge;]3) were recruited between August 2023 and February 2024; 84.1% (1233 participants) completed the primary outcome measure. InterventionsThe intervention group (IG) received the Smoke Free app including behaviour-change missions and gamification elements, while the control group (CG) received a text-only cessation information app. Both groups received brief advice from a healthcare professional. Main outcome measuresThe prespecified primary outcome was self-reported 7-day point-prevalence abstinence from combustible tobacco at 6 months post-randomisation; secondary outcomes included biochemical validation of abstinence in participants providing a saliva sample (59% of eligible participants). ResultsSelf-reported abstinence (primary outcome) was significantly higher in the IG compared with the CG (283 [39.3%] vs. 182 [24.4%], OR=2.01, 95% CI 1.60 to 2.50, p<0.0001). The NNT was 6.7 (5.1 to 9.8). The effect was consistent with biochemical validation (OR=1.76, 95% CI 1.27 to 2.44, p<0.0001) and across secondary outcomes and sensitivity analyses. The 6-month follow-up rates for the primary outcome did not differ between groups (IG: 601 [83.5%]; CG: 632 [84.7%]; p=0.52). Eighty-four serious adverse events were reported by 75 participants (IG: 31, 4.3%; CG: 44, 5.9%; p=0.53); none were treatment-related. ConclusionsThe Smoke Free app is effective for aiding smoking cessation in at least moderately dependent cigarette smokers compared with an informational app when provided as an adjunct to brief advice from a healthcare professional. Trial registrationThe trial was registered with the German Clinical Trials Register (DRKS00031140). FundingSmoke Free 23 GmbH (for-profit company).

BackgroundRising kidney discard rates and uncertainty around accepting higher risk donor kidneys highlight the need for decision support tools that integrate donor and recipient factors and communicate risk in ways that are understandable and usable at the time of offer. Conventional indices (e.g., KDPI/KDRI) provide population level signals but do not deliver individualized, cognitively accessible information aligned with real time clinical workflows. ObjectiveTo describe how key transplant stakeholders (patients, coordinators, and providers) interpret and evaluate a prototype Kidney Risk Calculator app that generates donor-recipient specific survival projections and to identify the content, format and features, and functionality needed for clinically meaningful, patient-centered decision support. DesignQualitative study using focus groups and individual interviews. SettingUniversity of New Mexico Hospital (UNMH) Kidney Transplant Center. ParticipantsFive patients (four transplant candidates and one patient advocate), three transplant coordinators, and five transplant providers (3 attending physicians and 2 advanced practice practitioners). MethodsSemi-structured sessions (45 to 60 minutes) with 13 stakeholders (patients, coordinators, and providers) included a live app demonstration and explored usability, interpretability, contextual information needs, perceived clinical utility, and anticipated barriers/facilitators. Data were collected via one coordinator focus group, one patient focus group, and five provider interviews; sessions were recorded, transcribed, de-identified, and analyzed using inductive reflexive thematic analysis. ResultsStakeholders affirmed the value of personalized projections as an adjunct to clinical judgment, particularly for higher risk offers. Participants prioritized: 1) Content: clear education on hepatitis C virus (HCV) positive donors and Public Health Service (PHS) risk criteria; plain explanations of Calculated Panel Reactive Antibody (CPRA); and framing that makes time on dialysis and tradeoffs salient; 2) Format & Features: plain language narratives, percentages rather than decimals, simple visuals, minimized acronyms, U.S. customary units, and a stepwise (TurboTax-like) input flow preferred by patients; and 3) Functionality: attention to cognitive load and workflow alignment, given phone based time pressure and digital access constraints. Stakeholders emphasized that the value of the tool hinges on clarity, context, and workflow fit, not predictive accuracy alone. LimitationsSingle center, formative prototype study with a modest sample; findings are illustrative and may have limited transferability. Participants reacted to a demonstration rather than using the app during real time offer calls; convenience/email recruitment and Zoom only English sessions may introduce selection bias; team involvement in app development may contribute residual confirmation bias despite mitigation. ConclusionsEarly stakeholder input suggests that a kidney offer decision support tool should integrate individualized predictions with plain language explanations, contextual information that addresses common misconceptions, workflow aligned functionality, and accessible outputs. Tools designed and implemented with these features may support acceptance of medically complex kidneys and may help reduce offer bypass and organ discard. These inferences reflect stakeholder perceptions in a formative qualitative study and warrant prospective evaluation.