Epidemiology

Selection bias is a common concern in causal inference. Two mechanisms can give rise to selection bias: collider bias or stratifying on an effect modifier of the exposure-outcome causal effect. Research into the impact of selection bias on instrumental variable (IV) analyses has focused primarily on collider bias. Here, we show that IV estimates can be biased if selection is an effect modifier (or caused by an effect modifier) of the exposure-outcome effect. In a simulation study, we investigate the size and direction of the bias when estimating effects on the mean difference or odds ratio scale. Bias can also occur due to heterogeneity in the instrument-exposure relationship, although its magnitude is typically smaller. Finally, we discuss methods to deal with this bias in practice, including inverse probability weighting (IPW).

Importance. Observational studies dominate evidence in cardiovascular outcomes research, yet no widely adopted reporting standard requires authors to disclose how much of an observed association is attributable to systematic error. The negative-control calibration framework of the Observational Health Data Sciences and Informatics consortium returns a binary answer: a calibrated P-value either crosses 0.05 or it does not. That answer conflates "is there a signal" with "is the signal credible." Objective. To define the Bias Attribution Fraction (BAF), a continuous estimator in [0, 1] that quantifies the fraction of an observed log relative risk that is attributable to systematic bias, together with its 95% credible interval, and to evaluate whether reporting calibrated P-value plus BAF plus interval width produces more defensible decisions than reporting the calibrated P-value alone. Design. Three-part study: (i) metric definition and interval construction; (ii) Monte Carlo simulation across 1920 conditions by 1000 repetitions = 1,920,000 simulated target estimates (a primary grid plus a sign-flipped companion grid of identical size, pooled for all analyses), with truth known by construction; (iii) two-arm beta-blocker target trial emulation in MIMIC IV, adhering to the TARGET guideline, with targeted maximum likelihood estimation (TMLE) and 12 negative-control outcomes. Setting. MIMIC IV (critical care) and MIMIC IV ECG, 2008 to 2019. Participants. Adults with a first intensive care unit stay and a recorded 12-lead ECG. Exposures. Two clinical questions are evaluated: (1) reaching at least 50% of guideline-recommended target dose of an index beta-blocker within 7 days of admission, sustained to 12 months, versus lower or no exposure; (2) receiving guideline-directed medical therapy (GDMT) with at least two of three foundational drug classes at adequate dose for 12 months versus fewer. Main Outcomes and Measures. One-year all-cause mortality. Primary analytic outputs: (i) the calibrated P-value from a negative-control empirical null distribution with 12 outcomes; (ii) BAF-hat with a Markov chain Monte Carlo 95% credible interval; (iii) a verdict drawn from a truth-calibrated lookup table mapping the BAF-hat by interval-width cell to a probability of being bias-dominated. Results. Across 1,920,000 simulated repetitions, the calibrated P-value alone yielded a misuse rate (declared usable while truth was bias-dominated) of 36.02%. Adding the BAF point estimate reduced misuse to 16.48%, and requiring the whole credible interval of BAF to lie below 0.5, clear of the bias-dominated zone, reduced misuse to 10.16%. Requiring in addition a calibrated probability of bias dominance below 15% reduced misuse to 3.02% at the cost of declaring 22.1% of estimates usable (versus 61.8% for the calibrated P-value alone). Read from the bias-dominated view, meaning that withholding a repetition is a positive call and being truly bias-dominated is a positive truth, the BAF estimator achieved an area under the receiver operating characteristic curve of 0.922, compared with 0.787 for the calibrated P-value. In MIMIC IV, question 1 produced a calibrated P-value of 0.894 and BAF-hat = 0.875 (95% credible interval 0.385 to 0.992); the apparent one-year mortality reduction of 16.9% was entirely attributable to systematic bias and the association was not usable as effect evidence. Question 2 also failed layer 1, with a calibrated P-value of 0.1026, and BAF-hat = 0.460 placed it in the "competitive, no verdict" zone. Conclusions and Relevance. Pairing the calibrated P-value with BAF and its 95% credible interval turns a binary credibility judgement into a three-channel report (signal existence, effect size, and bias fraction). Despite TARGET-adherent design, doubly robust TMLE, and 12 negative controls, both clinical questions in this study still failed to qualify as effect evidence, with question 1 demonstrating complete bias domination and BAF quantifying the systematic error that remained. The reporting standard proposed here can be applied to any observational study that includes negative controls, without requiring additional data.

Researchers must often rely on observational data to draw conclusions about the causal relationship between an exposure and an outcome. This is common in environmental epidemiology research, where the assignment to an exposure can be unethical or impossible. Consider a setting in which we seek to estimate excess mortality during heat waves, but are unable to randomize individuals to moderate or extreme heat. We propose to follow a causal framework for non-randomized time series that uses what we call historical time series matching to construct hypothetical randomized experiments, in which background covariates are balanced. We applied this framework and assessed the following causal question: Are daily mortality counts among elderly Americans higher during hot versus warm summer time periods? We addressed this causal question by drawing frequentist, Fisherian, and Bayesian inferences for five American cities. We estimated the average treatment effect of heat on three-day mortality with 95% Fisherian intervals to be 0.5 [-3.6, 4.4] deaths in Pittsburgh, 3.7 [0.7, 6.6] deaths in Seattle, 12.5 [6.1, 18.9] deaths in Chicago, 8.5 [1.4, 15.6] deaths in Los Angeles, and 20.3 [11.2, 29.5] deaths in New York.

Abstract Routine blood tests can be used to calculate several inflammation-related ratios, including the neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and platelet-to-lymphocyte ratio (PLR). These indices are widely reported as markers of cardiovascular risk, but it is unclear whether they provide comparable information or whether SII, which is derived from platelet count and NLR, adds anything beyond NLR. We analyzed two independent samples from the National Health and Nutrition Examination Survey (NHANES): 15,141 adults from 2005-2010 (development cohort) and 15,392 adults from 2011-2016 (validation cohort), each with complete blood count data and linked mortality follow-up. Survey-weighted Cox models, restricted cubic splines, multiple imputation, and model comparison were used, and the development model was applied to the validation cohort. During a median follow-up of 11.4 years, 795 cardiovascular deaths occurred in the development cohort; the validation cohort recorded 364 cardiovascular deaths over 5.75 years. In the development cohort, hazard ratios per 1-SD increase were 1.32 (95% CI 1.21-1.44) for NLR, 1.21 (1.12-1.32) for SII, and 1.13 (1.04-1.22) for PLR. When NLR and SII were entered together, only NLR remained associated (HR 1.49, 1.23-1.81). The validation cohort showed the same pattern: NLR was strongest (HR 1.35, 1.22-1.49), and neither SII nor PLR remained significant after mutual adjustment. The frozen model showed good discrimination (C-index 0.8991 to 0.9023) and calibration (slope 0.96). Adding NLR produced only a small improvement in discrimination, and reclassification results were inconsistent between cohorts.

In systematic review, the measures reported by the primary studies may not always be the measures sought by the systematic review investigators. Motivated by an systematic review of interventions to reduce meat consumption, we discuss here methods to infer relative risks and confidence intervals from randomized controlled trials that reported differences, possibly in the presence of repeated measures or multiple interventions.

Background: Quantifying behavioral drivers of protective action during infectious disease outbreaks remains challenging. App-based experimental epidemic games (epigames) provide a framework for studying these decisions in naturalistic settings, but correspondence between in-game measures and real-life beliefs or behaviors requires evaluation. We examined whether an activity-based opportunity cost influenced voluntary quarantine decisions within an epigame and whether real-life health beliefs transferred into the game and predicted observed choices. Methods: We conducted a two-week randomized controlled trial from May 4 to May 17, 2026, at VinUniversity (VinUni) in Hanoi, Vietnam. Participants using the Epigames app installed in their smartphone were randomized to a low-barrier group, earning 1 point per qualifying contact with other participant when remaining active, or a high-barrier group, earning 2 points per contact. Of 439 enrolled participants, 283 were successfully randomized to one of the two groups, made at least one valid daily quarantine decision, and their data was used in the analysis. Real-life (S1) and in-game (S2) beliefs measured susceptibility, severity, quarantine self-efficacy, and perceived benefits. Belief correspondence was assessed using Holm-adjusted Spearman correlations and prospective cumulative-logit models. Quarantine choices were analyzed using participant-day binomial generalized estimating equations (GEE) and participant-level quasi-binomial models. Results: Participants made 1,823 valid daily decisions and chose quarantine on 458 participant-days (25.1%). Quarantine rates were 30.1% in the low-barrier group and 20.2% in the high-barrier group. The high barrier was associated with 41% lower population-averaged odds of quarantine (GEE odds ratio [OR] 0.59, 95% confidence interval [CI] 0.40-0.86, = 0.0059); the quasibinomial estimate was identical (OR 0.59, 95% CI 0.40-0.86, = 0.0055). Among 251 participants with matched S1-S2 data, all four corresponding beliefs were positively associated (Spearman's = 0.259-0.418; all Holm-adjusted < 0.001). Prospective ordinal models confirmed positive matched-domain associations (ORs 1.39-2.18 per one-point S1 increase). Beliefs showed little robust association with quarantine behavior, and neither S1 nor S2 beliefs jointly modified the randomized effect. Self-efficacy, network degree, and the preregistered gender main effect were also unsupported. Conclusion: A higher activity-based opportunity cost reduced voluntary quarantine within the VinUni epigame, while participants carried relative differences in stated health beliefs from real-life to in-game framing. These beliefs did not reliably predict observed quarantine choices within the game framing. Epigames can support controlled study of incentives and belief correspondence in naturalistic settings such as college campuses, but behavioral validity or real-world generalizability requires further work.

The efficacy of current antiretroviral interventions for prevention of vertical HIV transmission is both a triumph and a challenge for future efficacy studies of novel agents, including long-acting antiretroviral agents. Given the effectiveness of current antiretroviral interventions, standard randomized controlled trials to assess novel interventions targeted towards further reducing vertical HIV transmission may be infeasible due to required sample sizes. To address this study design challenge, we propose a novel augmented clinical trial design, where a randomized trial with an active comparator is augmented with a concurrent observational cohort to supplement the standard-of-care arm. The proposed design and analysis can overcome sampling biases, and an associated diagnostic can evaluate potential biases from unmeasured covariates. The proposed trial design is illustrated with an example and studied further using a simulation study. In the illustrative example, an augmented trial with a 3:1 allocation ratio in the trial component yielded an unbiased treatment effect estimate, as compared to the trial component alone, and the standard error was reduced substantially. Although the total sample size of the augmented trial was larger than a similarly powered 1:1 standard trial, the sample size of the augmented trial's trial component was half the size of the standard trial and within the range of historically similar trials, thereby providing both feasibility and potential cost advantages. Although limitations with this design remain, many of these limitations can be mitigated by careful study planning and coordination. Augmented trials provide a potentially applicable solution to studying the efficacy of long-acting antiretrovirals for infant postnatal prophylaxis against HIV.

Estimating the correlation between subject-specific rates of change is a common objective in longitudinal studies. A natural approach is to fit ordinary least squares regressions per subject and correlate the resulting slope estimates directly. Under complete data, this slopes-first estimator is algebraically equivalent to a contrast-based plug-in estimator derived from the joint variance-covariance matrix of the two signals across time. Under independent dropout, however, the slopes-first approach implicitly applies heterogeneous weights across missing-data patterns, introducing bias whenever the cross-covariance between the two signals is not constant over time. We formalise this result and propose an unbiased alternative that estimates the joint covariance matrix before applying the contrast. We further extend our results to include covariates as well. Simulations confirm that the slopes-first estimator is biased under non-constant cross-covariance while the proposed alternative remains unbiased. Further, we show in a real-world example that the two estimators can lead to different scientific conclusions and recommend that researchers working with longitudinal data inspect the missing data pattern to make an informed decision about the estimator of choice.

Background: Multiple performance measures are used to assess the management of common cardiovascular conditions, such as coronary artery disease (CAD), heart failure with reduced ejection fraction (HFrEF), and atrial fibrillation (AF). These measures are sometimes used to assess individual clinicians, despite lack of evidence of reliability and accuracy. We used national Veteran Affairs (VA) data to test medication-based performance measures for assessment of clinician performance in outpatient cardiology care. Methods: This retrospective study examined VA cardiology clinician/Veteran dyads from 2017-2023 to evaluate 10 medication-based performance measures for CAD, HFrEF, or AF with elevated stroke risk. Achievement of each performance measure was defined as an active prescription of an indicated medication for each eligible patient encounter, summarized at the clinician level. Reliability was calculated using the beta-binomial method. A Bayesian causal model was used to assess the impact of case complexity on performance measure achievement. Finally, we performed peer review of a subset of cases (n=952) to assess the accuracy of three representative performance measures. Results: Of 456,071 unique cardiology encounters, 312,331 included CAD, 224,599 included HFrEF, and 178,678 included AF. These patients were treated by 1,043 cardiology clinicians. Median performance measure achievement ranged from 0.23 (SGLT2i for HFrEF) to 0.99 (beta blocker for HFrEF). Measure reliability varied from 0.63 to 0.90. Case complexity did not significantly affect performance measure achievement. Using peer review as the gold standard, accuracy was poor to moderate (balanced accuracy 0.51-0.73), and measures underestimated performance relative to peer review. Conclusions: Ten medication-based performance measures for CAD, HFrEF, and AF can be efficiently measured using electronic health record data, and most have sufficient reliability to support clinician benchmarking and the identification of potential care gaps. However, suboptimal accuracy compared with a peer review indicates that these measures may be insufficient as a standalone assessment of clinician quality.

Background: Maternal cigarette smoking is associated with adverse birth outcomes, but low birthweight may reflect preterm delivery, restricted fetal growth, or both. Contemporary evidence distinguishing smoking trajectories is limited. Objectives: To examine associations between maternal cigarette-smoking trajectories and low birthweight among U.S.-resident singleton live births in 2023 to 2024. Methods: We analyzed 5,861,166 U.S.-resident singleton live births in the 2023 to 2024 National Vital Statistics System natality files. Low birthweight was defined as birthweight <2,500 g. Trajectories were never smoking, quitting before pregnancy, quitting during pregnancy, persistent smoking, or other/intermittent smoking. Modified Poisson regression estimated adjusted risk ratios (aRRs) and 95% confidence intervals controlling for birth year and maternal, pregnancy, and infant characteristics. Results: Low-birthweight prevalence was 6.4% among never smokers, 7.7% among mothers who quit before pregnancy, 11.2% among those who quit during pregnancy, 14.6% among persistent smokers, and 10.2% among those with other/intermittent smoking. Compared with never smoking, adjusted risks were higher for quitting before pregnancy (aRR 1.06, 95% CI 1.03, 1.09), quitting during pregnancy (aRR 1.46, 95% CI 1.42, 1.51), persistent smoking (aRR 1.95, 95% CI 1.93, 1.98), and other/intermittent smoking (aRR 1.36, 95% CI 1.25, 1.49). Associations varied little by year (joint interaction P = 0.102). Conclusions: Persistent smoking showed the strongest association with low birthweight. Because the outcome combines preterm delivery and impaired fetal growth, the estimates should not be interpreted as associations with fetal growth restriction alone. Keywords Cigarette Smoking; Smoking Cessation; Pregnancy; Low Birthweight; Birthweight; United States

Background Cardiovascular prevention is organized around target metrics. Blood pressure, LDL cholesterol, and A1c define treatment targets, quality metrics, and, implicitly, who is considered safe. Yet a value below a cutoff is a correlative measurement, not an outcome. We tested whether social context recorded before the measurement separated subsequent cardiovascular events among people whose numbers already met the cutoff. Methods In this retrospective cohort study, we analyzed All of Us participants without recorded cardiovascular disease and with low comorbidity in three separately indexed cohorts: systolic blood pressure (SBP), LDL cholesterol, and A1c. A preindex score combined household income, education, food security, physical activity, and ZIP-linked deprivation, standardized against a fixed pooled reference of 325,652 eligible people. The outcome was first myocardial infarction, ischemic stroke, or heart failure. A sensitivity analysis indexed participants at only events recorded in inpatient or emergency settings. Results The SBP, LDL, and A1c cohorts included 19,525, 16,578, and 18,539 participants with 144, 129, and 191 events. Each one-SD more favorable score was associated with lower hazard: HR 0.611 (95% CI 0.498-0.749), 0.631 (0.512-0.778), and 0.544 (0.457-0.648), respectively. Below each study cutoff (SBP <140 mmHg, LDL <100 mg/dL, A1c <8%), less favorable context had event rates of 3.39 versus 1.68, 7.16 versus 2.05, and 8.67 versus 2.34 per 1,000 person-years. In a one-year analysis restricted to 2019-2023 index dates, the corresponding adjusted HRs were 0.530, 0.336, and 0.365. In the sensitivity cohorts (54,338, 41,451, and 33,607 participants; 493, 288, and 316 events), HRs were 0.732 (0.657-0.815), 0.660 (0.573-0.761), and 0.631 (0.552-0.722). Conclusions A biomarker below the study cutoff did not identify a uniform, low-risk population. Social context recorded before the measurement separated cardiovascular events after adjustment for the measured value and clinical factors. Risk assessment that stops at the number misses this variation.

Background State-level estimates of hepatitis C virus (HCV) prevalence are needed to guide resource allocation and to provide a baseline against which to measure progress toward elimination goals. Population prevalence is not directly observable, requiring estimation from indirect data sources. Methods We adapted a Bayesian spatial integrated abundance model to estimate state-level HCV prevalence during 2017-2020 across 48 states and the District of Columbia. The model integrated six HCV-related outcomes: acute and chronic surveillance cases, HCV-related deaths, observations of HCV in [MarketScan] administrative claims data, diagnoses of HCV in Medicaid recipients, and treatment with direct-acting antivirals in Medicaid recipients. Estimates were anchored to a national prevalence estimate, and the model accounted for data source-specific selection, heterogeneity in HCV risk factors across states, and geospatial correlation. Results Estimated average prevalence was 1.32% (95% credible interval [CrI]: 0.94%-1.80%), corresponding to 3.33 million (95% CrI: 2.37-4.65 million) adults with HCV infection across 48 states and DC. State estimates ranged from 0.74% in North Dakota to 2.22% in Oklahoma (median state-specific prevalence, 1.27%), with higher prevalence concentrated in South Central states, Appalachia, and the West, and the lower prevalence in the upper Midwest, Southeast, and New England. Ten states accounted for 56% of estimated infections. Estimates were stable in sensitivity analyses (most differences <1 percentage point). Conclusions These estimates quantify state-level HCV burden prior to the 2021 federal expansion of HCV surveillance funding, providing a baseline for monitoring elimination progress. Geographic variation indicates that resource needs will differ by jurisdiction.

Elevated systolic blood pressure (SBP) is a major modifiable driver of cardiovascular morbidity. Digital food-as-medicine and home-blood-pressure-monitoring programs increasingly support its management outside the clinic, but most published evaluations are single-arm, pre/post designs; because enrolment is conditioned on an elevated baseline reading, regression to the mean (RTM) is expected to inflate any observed improvement, and this is rarely checked against an internal comparator. We conducted a retrospective, single-arm cohort study of home blood-pressure monitoring within a commercial digital food-as-medicine program serving a US employer-sponsored population with obesity. Of 329 members included, 136 were at-goal (<130/80 mmHg), 74 at Stage 1 (130-134/80-84 mmHg) and 119 at Stage 2 ([&ge;]135/85 mmHg) at baseline; follow-up SBP was evaluable for 128, 68 and 112 members in these strata at 3 months and for 62, 34 and 53 at 6 months, respectively. The primary estimand was the unadjusted within-member change in home SBP at 6 months among members with Stage-1 and Stage-2 blood pressure at baseline; a secondary estimand corrected for RTM using an ANCOVA linear mixed model with the at-goal stratum as an internal negative-control population. Among Stage-1 members (n = 34), unadjusted SBP change was -4.36 mmHg (95% CI -7.43 to -1.28; p = 0.0069), paired with an RTM-corrected secondary change of -3.33 mmHg (-6.40 to -0.26; p = 0.033). Among Stage-2 members (n = 53), unadjusted change was -10.17 mmHg (-12.94 to -7.40; p < 0.001), paired with an RTM-corrected secondary change of -5.50 mmHg (95% CI -8.50 to -2.50; p = 0.00037); RTM accounted for ~24% and ~46% of the unadjusted decline in Stage-1 and Stage-2, respectively. RTM-corrected estimates were robust to sensitivity analyses on measurement modality, seasonality, covariates and model specification, with no evidence of attrition bias. Program participation was associated with a decline in home SBP among Stage-1 and Stage-2 members that remained significant, though smaller, after RTM correction, supporting RTM-adjusted SBP change as the appropriate metric for evaluating food-as-medicine interventions.

Background. Coronary CT angiography (CCTA) and calcium scoring detect subclinical coronary atherosclerosis, but the results of community based screening in asymptomatic adults with cardiovascular risk factors are poorly described. Objective. To describe the results of the first phase of a county-funded CT screening program and their variation by demographics, health system, time, and ZIP code. Methods. Retrospective analysis of aggregate reports from the Broward County Florida Heart Project (May 2023 to September 2025). Eligible residents were those with insurance coverage, age 45 to 70 years (limited exceptions), free of known cardiovascular disease and symptoms, with at least one risk factor. Examinations at six hospitals in four health systems were classified by the program team as normal (Category 1), abnormal (Category 2), or very abnormal (Category 3), with a separate flag for other non coronary abnormality. We used Wilson confidence intervals and chi square, trend, and Mantel Haenszel tests. Results. Among 7,135 participants (58.3% men; 79.0% White, 8.0% Black, 17.2% Hispanic), 43.2% had Category 1, 45.4% Category 2, and 11.4% Category 3 results; 56.8% (95% CI, 55.7% to 58.0%) had abnormal or very abnormal results. The proportion rose from 39.3% at 45 to 55 years to 78.5% at 71 or older (trend P<0.001) and was higher in men than women (68.9% vs 39.8%; odds ratio [OR], 3.36; 95% CI, 3.05 to 3.71). Rates were lower in Black than White participants (38.9% vs 58.6%; hospital adjusted OR, 0.45; 95% CI, 0.38 to 0.54) and in Hispanic than non Hispanic participants (51.0% vs 57.8%). Abnormal results ranged from 39.5% to 60.3% across health systems (P<0.001). Variation across the 50 highest volume ZIP codes did not exceed chance (P=0.53). Conclusions. More than half of insured, asymptomatic adults with risk factors screened in this community program had abnormal results, with steep age and sex gradients and unexplained variation across health systems. Black, Hispanic, and female residents were under-represented relative to the county

Although physical activity is associated with better physical functioning, the effects of initiating regular physical activity among older adults with persistent inactivity remain unclear. We conducted a sequential target trial emulation using Health and Retirement Study (HRS) data to estimate the effect of physical activity initiation versus noninitiation on incident mobility difficulty. Participants were included in the analysis if they were aged [&ge;]65 years at the previous wave, reported no regular physical activity at the previous two waves, and had no mobility, activities of daily living (ADL), or instrumental activities of daily living (IADL) difficulties at baseline. Stabilized inverse probability of treatment weighting was applied to adjust for measured confounding. Weighted cumulative incidence was then estimated with death treated as a competing event, and risk differences were calculated at approximately 2, 4, and 6 years. At 2 years, the cumulative incidence of mobility difficulty was 32.75% among initiators and 39.73% among noninitiators, with a risk difference of -6.98 percentage points (95% CI, -13.11 to -0.88). At 4 and 6 years, risk differences were -2.90 percentage points (95% CI, -9.77 to 4.29) and -5.44 percentage points (95% CI, -12.27 to 1.99), respectively. Initiating regular moderate or vigorous physical activity was associated with a lower risk of incident mobility difficulty at approximately 2 years among older adults with persistent inactivity. This difference was less evident at later follow-up, and no clear differences were observed for incident ADL or IADL difficulty.

Introduction: Monitoring transitions in cigarette and electronic nicotine vapor product (ENVP) use is important for understanding how marketplace and regulatory changes have affected product use patterns, but information from national surveys is often delayed by several years. Methods: The Rutgers Omnibus Study is a quarterly serial, rapid-response, web-based convenience survey. Using a Markov multistate transition model, we estimated transition rates and one-year transition probabilities between never, non-current, ENVP-only, cigarette-only, and dual use among 5,292 adults aged 18-45 who participated in at least two of Waves 1-16 (February 2022-February 2026), as well as overall transition hazard ratios by sex, age group, and race & ethnicity in a multivariable model. Results: About 40% of individuals using cigarettes (39.5%; 95% CI: 36.9-42.0) or ENVP (40.1%; 95% CI: 36.4-43.7), and 60% of those using both products (60.8%; 95% CI: 58.3-63.2) were predicted to remain in those states after one year. Female participants began using cigarettes from never use (adjusted hazard ratio [aHR] 0.44; 95% CI: 0.29-0.66) or non-current use (aHR 0.65; 95% CI: 0.48-0.88) at lower rates than male participants. Participants aged 35-45 years initiated ENVP-only use from non-current use (aHR 0.34; 95% CI: 0.23-0.51) at a lower rate than those aged 18-34 years. Compared to non-Hispanic White participants, non-Hispanic Black participants initiated ENVP-only use from never use at a higher rate (aHR 4.39; 95% CI: 2.09-9.25). Conclusion: Rapid response surveys can provide important preliminary information on tobacco use transition patterns while we wait for the slower release of nationally representative survey data.

Background Anxiety, depression and menstrual problems are conditions that significantly impact an individual's health and wellbeing yet, barriers to healthcare can prevent access to appropriate medical care. Trust in doctors is a notable factor as previous studies have shown that it affects engagement with healthcare services and has declined in recent years. Methods Using the Avon Longitudinal Study of Parents and Children, we investigated associations between trust in doctors and healthcare access at two timepoints, for anxiety, depression (mothers and partners) and menstrual problems (mothers only). Results Mothers reporting greater trust in doctors were more likely to access care for anxiety (n=1849, adjusted odds ratio (AOR) 1.11, 95% confidence interval (95% CI) 1.08-1.14), depression (n=2411, AOR 1.10, 95% CI 1.07-1.12), and menstrual problems (n=1723, AOR 1.04 95% CI 1.01-1.06). If mothers and partners endorsed 'the doctor is always helpful', they were more likely to access care for depression (mothers: n=2005, AOR 1.30, 95% CI 1.16-1.46; partners: n=580, AOR 1.46, 95% CI 1.05-2.03) and for anxiety (mothers: n=1892, AOR 1.35, 95% CI 1.19 to 1.54; partners: n=734, AOR 1.40, 95% CI 1.02-1.91). Implications This study supports association between trust in doctors and healthcare access for anxiety, depression and menstrual problems, particularly amongst mothers. This contributes to previous literature that suggests that trust remains an important factor in understanding treatment gaps, and that researchers and clinicians must work to improve public trust in healthcare systems.

Financial well-being (FWB) encompasses objective and subjective financial status and may influence health. We developed and validated a parsimonious FWB index using data from 23,006 Health and Retirement Study (HRS) participants aged 50 years and older from 2006 to 2022. The FWB-5 Index was derived using correlated two-factor confirmatory factor analysis accounting for household clustering. Indicators included household wealth, household income-to-poverty ratio, monthly financial strain, satisfaction with one's financial situation, and perceived financial mastery. We assessed convergent and discriminant validity using the Consumer Financial Protection Bureau Financial Well-Being (CFPB-FWB) Scale and Satisfaction with Life Scale, respectively. Associations with pre-baseline socioeconomic factors and subsequent depressive symptoms and health conditions were examined to assess construct and predictive validity. Model fit indices supported the two-factor structure (robust comparative fit index = 0.99; robust root mean square error of approximation = 0.05). Correlations were stronger with the CFPB-FWB Scale (Spearman P = 0.80) than with life satisfaction (Spearman P = 0.43). Associations with socioeconomic factors and subsequent health outcomes were in the hypothesized directions. These findings support the FWB-5 Index as a parsimonious measure for examining financial well-being and its associations with health across HRS waves, including those predating the CFPB-FWB Scale.

Background. Immune checkpoint inhibitors (ICIs) can cause immune-related adverse events in almost any organ. While myocarditis has received the most attention, data on pericardial disease with ICI use are limited. We examined pericardial adverse event reporting with nine ICIs using the FDA Adverse Event Reporting System (FAERS). Methods. We combined legacy AERS and current FAERS quarterly files from January 2011 to June 2026 into 18,629,240 unique cases, deduplicated by case number, and removed cases the FDA had listed as deleted. ICI exposure was identified from generic names, brand names, development code names and the active-ingredient field. Pericardial events were defined by seven MedDRA preferred terms. We estimated reporting odds ratios (ROR), proportional reporting ratios (PRR), Bayesian information components (IC) and empirical Bayes geometric means (EBGM). We also examined myocarditis co-reporting, time trends, a comparison with other antineoplastic agents, a MedDRA query-based outcome, and 14 alternative definitions. Results. The analysis included 18,514,045 cases: 277,434 involved an ICI, 34,792 carried a pericardial term, and 1,680 had both. Pericardial events were reported disproportionately with ICIs (ROR 3.35 (95% CI 3.19 to 3.52); PRR 3.34; IC025 1.62). Eight of nine agents had a ROR above 1 (3.32 to 5.63). Among the most frequently reported terms, estimates were highest for cardiac tamponade (5.59 (95% CI 4.98 to 6.28)), followed by pericardial effusion (3.83 (95% CI 3.60 to 4.07)) and pericarditis (2.09 (95% CI 1.89 to 2.31)). After removing cases that also reported myocarditis, the ROR was 3.13 (95% CI 2.98 to 3.30). Most sensitivity analyses gave similar estimates (ROR 3.13 to 3.47); defining exposure by generic drug names alone, which identified only 44% of exposed cases, gave 3.99. Compared with cases naming other L01 antineoplastic agents, the ROR was 1.13 (95% CI 1.08 to 1.19): 1.28 (95% CI 1.20 to 1.36) for ICI-only cases and 0.96 (95% CI 0.89 to 1.04) when another antineoplastic was also named. The ICI-specific reporting odds ratio fell each quarter (odds ratio 0.977 per quarter, 95% CI 0.973 to 0.980). Conclusions. In FAERS, pericardial events were reported more often with ICIs than with other drugs across most agents and terms; however, incidence and causation cannot be determined. The excess over other antineoplastic agents was small, and reporting disproportionality declined over time. These findings support clinical awareness of pericardial disease in patients receiving ICIs and warrant study in data that can measure incidence.

Background Timely overdose response depends not only on surveillance systems that detect overdose clusters, but also on communication pathways that deliver actionable information to the professionals positioned to respond. Understanding which overdose-related communications are received, perceived as useful, and translated into action is essential for improving overdose prevention and response infrastructure. Objective We assessed which overdose-related communication channels professionals reported receiving, which they perceived as most helpful, how organizations reported responding to overdoses or overdose clusters, and whether these patterns differed by professional role, county, and funding source. Methods We conducted an exploratory descriptive survey in 2024 using a convenience sample of professionals across Connecticut whose jobs involved overdose prevention or response (N=149). Using this survey data, we summarized reported receipt and perceived utility of overdose-related communication channels, organizational response actions, and internal receipt of Connecticut Department of Public Health overdose alerts. Results were stratified descriptively by professional role, county, and funding source. We interpreted findings using a public health communication-to-action cascade focused on signal detection, communication receipt, internal dissemination, perceived utility, and response action. Results Reported communication receipt and perceived utility differed most clearly by professional role. Peer information sharing was the most commonly reported communication source and was frequently perceived as helpful, but respondents who reported receiving formal channels such as state alerts, local alerts, or platform-based information often identified those channels as most helpful. Internal receipt of Connecticut Department of Public Health overdose alerts also varied by role. SSPs/substance use treatment programs and first responders more often reported outreach to people who use drugs, whereas government and health district respondents reported a more even mix of public communication and outreach activities. Conclusions Overdose-related communication in Connecticut appears role-dependent and unevenly distributed. Informal peer networks are widely used and perceived as helpful, while formal alert systems may require improvements to reach and actionability. Practical improvements include opt-in communication pathways tailored to professional roles, clearer access points for state and local alerts, internal dissemination protocols that specify who should receive which alerts, and alert-to-action playbooks that connect specific alert types to recommended response steps.