American Journal of Epidemiology

Background Population research increasingly relies on integrating data across studies to understand patterns of health and disease at broader scales. Such integration expands sample size and supports multimodal analyses, but independent cohort studies are often not directly comparable due to differences in instruments, timing, and measurement structure. Data harmonization addresses this challenge by transforming heterogeneous variables into comparable measures, allowing datasets collected under different protocols to be analyzed jointly, but published harmonization efforts seldom include an analysis conducted on the integrated dataset itself. Methods We developed an integrated dataset, combining two longitudinal cohorts, the Boston Puerto Rican Health Study (BPRHS) and the Puerto Rico Observational Study of Psychosocial, Environmental, and Chronic Disease Trends (PROSPECT). Comparability between variables was established through an explicit framework distinguishing availability from construct comparability, and the evidence supporting comparability is reported by class of feature, including tests of measurement invariance and differential test functioning for the multi-item psychosocial scales. To support transparency and reuse, we provide an open-source Python pipeline and structured metadata describing variable alignment and transformations. As a worked example of downstream use, we conducted a Random Forest analysis predicting diabetes status. Results The integrated dataset comprises 539 harmonized variables across four BPRHS visits and two PROSPECT visits, each documented with its source variable, question wording, and transformation. In the worked example, the integrated dataset retained predictive signal relative to cohort-specific models and improved performance for the smaller cohort. Conclusions These findings illustrate how transparent, documented cross-cohort integration can support predictive population health research while making the basis for comparability explicit.

Background: Excess mortality estimates depend on an expected baseline, an unobservable quantity typically derived from models with prescribed functional forms and short reference periods that exclude exceptional events. Singular Spectrum Analysis (SSA) offers a nonparametric alternative but requires subjective component selection and is sensitive to outliers. We combined SSA with automated component selection and outlier interpolation to estimate long-term baselines and compile a catalog of excess mortality events (EMEs). Methods: We analyzed weekly all-cause mortality (2015-2024) for six metropolitan areas with contrasting climatic, demographic, and epidemiological contexts: Brussels, Antwerp, and Liege (Belgium), and Athens, Thessaloniki, and Larisa (Greece). SSA was applied to standardized log-mortality rates; components were selected using Monte Carlo SSA, and COVID-19 periods were interpolated iteratively. EMEs were defined as weeks exceeding a quasi-Poisson two-z-score threshold, then screened for coincidence with heat waves, cold waves, COVID-19 periods, and influenza epidemics. Results: Baselines were consistent within countries: annual cycles in Belgium, with an additional significant semiannual cycle in Greece reflecting summer mortality. We cataloged 170 EMEs spanning 456 weeks and 14,210 excess deaths. Pandemic weeks accounted for 77% of excess mortality, and 96 weeks coincided with temperature extremes; 56 EMEs, representing 3.7% of excess mortality, remained unmatched. Conclusions: SSA yields reproducible, low-assumption baselines that capture country-specific seasonality. Because flexible baselines can absorb recurrent seasonal mortality, component selection is a substantive choice that should be reported explicitly, together with its sensitivity.

Background: Retirement is a major life transition that may influence cognitive aging. Retirement pathways are becoming increasingly diversified, yet evidence on how heterogeneous retirement trajectories relate to dementia risk remains limited. This study used sequence and cluster analysis to characterize thousands of transition-to-retirement trajectories, including retirement timing and work intensity, to estimate associations with dementia risk. Methods: We used data from 13,879 participants in the U.S. Health and Retirement Study. Our exposure was transition-to-retirement trajectories between ages 51 and 75 identified using sequence and cluster analysis. Our outcome was a validated algorithmically derived dementia probability measure. We estimated associations between trajectory clusters and dementia risk beginning at age 76 using generalized estimating equations, adjusting for sociodemographic characteristics. Inverse probability weights were used to account for attrition. Results: We identified ten distinct transition-to-retirement trajectories characterized by differences in timing of retirement, part-time work, disability, and labor force attachment. Compared with individuals retiring after full-time work in their early-to-mid 60s, dementia risk was similar among those retiring earlier or later from full-time work. In contrast, individuals with sustained part-time employment between ages 51 and 75 had lower odds of dementia (OR: 0.62, 95% CI: 0.48, 0.80), while trajectories characterized by prolonged disability were associated with higher odds of dementia (OR: 1.53, 95% CI: 1.22, 1.91). Conclusions: Retirement timing alone showed limited association with later-life dementia risk, whereas sustained part-time employment and disability-related trajectories were more strongly associated with dementia risk. These findings suggest that employment intensity and health-related exit from the labor force may be more relevant for dementia risk than retirement timing, and that gradual and flexible work-to-retirement transitions may benefit brain health. Background: Retirement is a major life transition that may influence cognitive aging. Retirement pathways are becoming increasingly diversified, yet evidence on how heterogeneous retirement trajectories relate to dementia risk remains limited. This study used sequence and cluster analysis to characterize thousands of transition-to-retirement trajectories, including retirement timing and work intensity, to estimate associations with dementia risk. Methods: We used data from 13,879 participants in the U.S. Health and Retirement Study. Our exposure was transition-to-retirement trajectories between ages 51 and 75 identified using sequence and cluster analysis. Our outcome was a validated algorithmically derived dementia probability measure. We estimated associations between trajectory clusters and dementia risk beginning at age 76 using generalized estimating equations, adjusting for sociodemographic characteristics. Inverse probability weights were used to account for attrition. Results: We identified ten distinct transition-to-retirement trajectories characterized by differences in timing of retirement, part-time work, disability, and labor force attachment. Compared with individuals retiring after full-time work in their early-to-mid 60s, dementia risk was similar among those retiring earlier or later from full-time work. In contrast, individuals with sustained part-time employment between ages 51 and 75 had lower odds of dementia (OR: 0.62, 95% CI: 0.48, 0.80), while trajectories characterized by prolonged disability were associated with higher odds of dementia (OR: 1.53, 95% CI: 1.22, 1.91). Conclusions: Retirement timing alone showed limited association with later-life dementia risk, whereas sustained part-time employment and disability-related trajectories were more strongly associated with dementia risk. These findings suggest that employment intensity and health-related exit from the labor force may be more relevant for dementia risk than retirement timing, and that gradual and flexible work-to-retirement transitions may benefit brain health.

Doubly robust survival estimators require nuisance models that the available information can support. We examine design and estimator dependence in TCGA breast cancer data. Treatment indicators disagree for 337 of 1076 patients; 67.4% have unobserved five-year binary outcomes; and receptor imputation expands the triple-negative subgroup from 156 directly measured patients to 226. We compare chemotherapy initiation by day 90 with non-initiation, using adjusted restricted mean survival time (RMST) over the next 1095 days. Weighting-only estimates are 55 days (95% bootstrap interval: -24, 133) in triple- negative disease (16 deaths) and 38 days (12, 72) in the whole cohort (72 deaths). A Cox outcome-model benchmark yields 108 and 35 days, respectively, demonstrating material model dependence in the smaller population. A parameter-to-information ratio summarizes the burden on complete-outcome regressions within cross-fitting. Its simulation-calibrated warning level is 0.31 (0.28 - 0.35) for the original regime; additional simulations examine covariate-dependent censoring, delayed benefit and nonlinear misspecification. The ratio does not certify estimator validity or replace weight diagnostics. The contribution is a reproducible workflow that separates design, nuisance-model support and estimator stability before interpreting observational survival contrasts.

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).

Purpose: Accurately capturing a patient's health journey is essential for ensuring the validity of scientific and public health studies. Electronic health records (EHR) and administrative claims are commonly used in healthcare research, albeit separately. Previous research has demonstrated that EHR and claims capture different aspects of patient history and care, potentially introducing bias when used alone. In this study, we leveraged the largest public US EHR resource, the National Clinical Cohort Collaborative (N3C), alongside Centers for Medicare & Medicaid Services (CMS) claims data to compare their information coverage and agreement. Methods: We integrated N3C EHR and CMS data to create one cohesive dataset and quantified the proportion of key variables present in EHR alone, CMS alone, and the overlap present in both data sources. Overlap in information coverage was quantified pre- and post-landmark diagnosis of COVID-19. To evaluate the impact of data coverage differences, we conducted a case study evaluating the effect of COVID-19 vaccination on ischemic stroke using both data sources alone and in combination. Results: CMS data helped identify many individuals that were duplicated among EHR identifiers. Examination of the overlap showed that CMS data more completely captured conditions, while EHR data more completely captured medications after COVID-19 diagnosis. Some variables exhibited marked differences over time after landmark COVID-19 diagnosis. Using target trial emulation and self-controlled case series methods, we found that data capture variations altered effect estimates: EHR data alone yielded lower risk of ischemic stroke after vaccination in the target trial emulation (odds ratio 0.47, 95% CI 0.28-0.79), with a smaller, non-significant reduction in the self-controlled case series (incidence rate ratio 0.81, 95% CI 0.64-1.02), whereas analyses using CMS data alone or both sources combined yielded estimates close to the null in both designs. Conclusions: Results indicated that when using either a national database combining multiple EHR sources, or CMS claims data alone, patients' medical histories are less completely captured compared to when these data sources are combined. Case study results highlighted the value of combining data sources for not only yielding fuller patient health histories and valid scientific results, but for identifying and explaining biases that may emerge in research studies that rely on a single data source.

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: Handgrip strength is associated with cardiometabolic abnormalities, but whether it adds information once detailed adiposity and muscle mass are known is unclear. Methods: We studied adults aged 40-59 years from NHANES 2011-2012 (development, n=508) and 2013-2014 (temporal validation, n=616). The outcome was at least two of four non-adiposity abnormalities (elevated blood pressure, fasting glucose or triglycerides, or low HDL cholesterol); waist circumference was not part of the outcome. A prespecified feature ladder added DXA body fat percentage and visceral adipose tissue to age, sex, BMI and waist circumference (M1), then appendicular skeletal muscle index (M2), then absolute (M3A, primary) or weight-normalised (M3B) grip strength. Logistic regression was primary; elastic-net and XGBoost were supportive. Development performance used nested cross-validation; frozen models were scored once in the validation cohort. Paired increments used 2000 stratified bootstrap resamples. Results: The outcome occurred in 249 (49.0%) and 277 (45.0%) participants. Adding adiposity increased the logistic regression AUC by 0.0209 (95% CI 0.0024-0.0402) in development and 0.0209 (0.0032-0.0375) in temporal validation. Adding absolute grip strength to M2 changed the AUC by -0.0046 (-0.0107 to 0.0013) and 0.0045 (-0.0009 to 0.0098), respectively. All 12 grip-strength AUC increments across algorithms, definitions and cohorts had intervals that included zero. Calibration was unchanged and the incremental net benefit was not consistently positive. Model attributions to grip strength varied by algorithm. Conclusions: In middle-aged adults, detailed adiposity, but not grip strength, added reproducible information for concurrent cardiometabolic risk stratification beyond conventional anthropometry and muscle mass.

Background: Vitamin D deficiency has been associated with mortality and chronic disease. In a recent study of Israeli Leumit Health Services (LHS) and US TriNetX populations, severe deficiency was reproducibly associated with mortality and multisystem morbidity, while longitudinal supplementation reconstructed from pharmacy dispensing was associated with lower risk. Methods: We sought to reproduce these associations and address a wide range of potential confounders in the deeply phenotyped UK Biobank. Baseline circulating 25-hydroxyvitamin D [25(OH)D] was standardized to a common late-winter nadir using an eight-harmonic model, and participants were categorized according to nadir-equivalent 25(OH)D as <5, 5-10, 10-15, 15-20, or [&ge;]20 ng/mL. Primary survival analyses used one-year delayed entry and complete-case multivariable adjustment. Results: Analyses included 412,020 participants and 31,197 deaths. Mortality hazard ratios were 2.03 (95% CI 1.90-2.18), 1.34 (1.29-1.39), 1.08 (1.04-1.11), respectively, for <5, 5-10 and 10-15, versus [&ge;]20 ng/mL. Associations previously observed in LHS and TriNetX were reproduced, notably for diabetes, myocardial infarction, cerebrovascular disease, dementia, diabetic retinopathy, and end-stage renal disease, whereas skin malignancies showed the opposite gradient, consistent with greater ultraviolet exposure. DAG-informed causal analyses using generalized overlap weighting achieved acceptable balance across measured covariates; weighted 10-year mortality was 9.7% with very severe deficiency versus 4.8% with [&ge;]20 ng/mL, supporting a causal interpretation. Estimates remained stable with landmark lags extending to 5 years after measurement. Conclusions: Seasonally normalized 25(OH)D identifies a reproducible state of systemic vulnerability while reducing exposure misclassification caused by calendar timing of measurement. An interactive implementation of the normalization model is available: https://vitamind.medil.ai.

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.

Background: Health Information Exchanges (HIEs) offer a potential resource for timely, population-level infectious disease surveillance by integrating electronic health record data across healthcare systems. Methods: We developed an end-to-end informatics pipeline for near-real-time community-level health surveillance based in a regional HIE serving Southeast Texas. We identified deidentified individual-level COVID-19, influenza, and RSV cases using diagnosis codes and laboratory results and assessed demographic and temporal trends from January 2022 through August 2026. Results: We identified 492,242 COVID-19 cases, 343,977 influenza cases, and 33,159 RSV cases during the study period. Surveillance data captured expected seasonal patterns, including recurring winter COVID-19 and influenza peaks and shifting RSV epidemiology, while enabling monitoring of laboratory test positivity metrics and hospitalization trends across age groups. Conclusions: HIE-based surveillance provides scalable, near-real-time monitoring of disease targets across large populations. HIE infrastructure can enhance public health situational awareness and complement traditional surveillance systems by harmonizing vital health data across systems and providers and delivering timely, standardized, and representative population health indicators.

Prior work has shown that school-age children's influenza activity precedes that of adults by one to two weeks. Whether this is robust across outcomes or constant across a season's course is unexamined. Using electronic health record data from two health systems in South Carolina (2016-2026) on individuals with an influenza diagnosis or positive test, we tested age-group lead-lag relationships across six measures using differenced cross-correlation and Granger causality, with significance confirmed via heteroskedasticity-robust block permutation. We tested whether relationships differed between a season's growth and decline phases. Children 5-17 years led every other age group by approximately one week in four out of six outcomes and were the only group showing any nonzero-lag relationship across 60 pairwise combinations. A screen of 150 source-target combinations identified 26 permutation-confirmed instances of children's lower-acuity activity preceding elevated emergency department or inpatient burden in other age groups. The same-outcome lead was concentrated during a season's growth phase, while the cross-severity lead was robust throughout both a season's growth and decline. School-age children's influenza activity is a robust, multi-outcome leading indicator with distinct transmission-timing and severity-progression signals in different phases of seasonal epidemics, supporting incorporation of age-stratified, phase-aware indicators into influenza forecasting.

Wildfire smoke exposure is increasing across the U.S., but the long-term neurological effects of smoke-specific fine particulate matter (PM2.5) and its carbonaceous components remain uncertain. Using Medicare data from 2002 through 2019, we examined incident Alzheimer's disease (AD) and Alzheimer's disease and related dementias (ADRD) among 39,678,630 fee-for-service Medicare beneficiaries followed from 2005 through 2019. We identified 3,501,414 incident AD cases over 258.5 million person-years and 8,377,416 incident ADRD cases over 248.6 million person-years. We linked the cohort with annual, high-resolution estimates of smoke and non-smoke PM2.5 mass and carbonaceous components (elemental and organic carbon) and fitted stratified Cox proportional hazards models. Per 1 g/m3 increase, smoke PM2.5 was associated with incident AD (hazard ratio [HR], 1.015, 95% confidence interval [CI], 1.005-1.026) and ADRD (1.029 [1.024-1.035]). Smoke carbonaceous fraction was also associated with AD (1.063 [1.042-1.085]) and ADRD (1.090 [1.077-1.103]). Within the ADRD endpoint, smoke-related associations exceeded their corresponding non-smoke associations on a per-unit-mass basis. Per interquartile range, associations were smaller for smoke PM2.5 (AD, 1.005 [1.002-1.008]; ADRD, 1.009 [1.008-1.011]) than for non-smoke PM2.5 (AD, 1.086 [1.079-1.094]; ADRD, 1.048 [1.043-1.053]). Associations varied across geographic, racial, socioeconomic, and clinical subgroups. Long-term wildfire smoke exposure, particularly its carbonaceous fraction, was associated with higher incidence of AD and ADRD among older U.S. adults. The associations correspond to 2,284 and 11,142 incident cases per year of AD and ADRD attributable to smoke PM2.5 among older Medicare beneficiaries, with the carbonaceous fraction accounting for most of this burden. These findings support the use of source-resolved and composition-specific smoke metrics in dementia research and wildfire-smoke health-impact assessments.

Understanding when and how pathogens spread from person to person is fundamental to characterizing transmission, interpreting surveillance data, and designing effective control measures. Yet key epidemiological delays -- including the latent period, incubation period, generation interval, and serial interval -- are difficult to estimate because key events such as infection, infectiousness, symptom onset, and transmission are rarely observed directly. Current approaches typically estimate these delays using flexible statistical distributions rather than explicitly linking them through the underlying processes that generate them, making it difficult to interpret the underlying biology and to ensure consistency across related delay estimates. Here, we develop a stochastic event-time framework built around a dynamic within-host pathogen-growth model that treats epidemiological delays as coupled outcomes of a shared biological process. By representing these key events as linked stochastic events, the framework produces internally consistent delay distributions while inferring parameters with mechanistic interpretations conditional on the assumed within-host model that govern pathogen growth, clearance, infectiousness, and symptom onset. The framework also accommodates censored and partially observed infection and symptom-onset times through a unified observation model. Across simulation studies, the framework accurately recovered generation-interval characteristics across sample sizes and disease time scales. In applications to SARS-CoV-2 and mpox transmission-pair data, the framework showed improved predictive fit relative to conventional lognormal delay models in several analyses, although the magnitude of improvement varied across datasets and observation models; it estimated shorter generation intervals for Omicron than Delta and revealed alternative within-host parameters compatible with the mpox transmission data. This mechanistic framework provides a biologically interpretable alternative to independently fitted delay distributions, yields internally consistent estimates of epidemiological delays, and establishes a foundation for integrating biological and epidemiological data to better understand, predict, and control pathogen transmission.

The 2025 measles outbreak in South Carolina, the largest in the United States in over two decades, produced 997 confirmed cases, predominantly among school-aged children in an under-vaccinated community in Spartanburg County. To support the South Carolina Department of Public Health (DPH) measles response, we developed a stochastic agent-based model to forecast outbreak trajectory and evaluate intervention strategies in real time, integrating transmission across three spatial scales and grounded in geocoded households, school enrollment records, and census-derived demographic data. The model predicted a median of 1,020 total infections (90% prediction interval: 840-1,273). Counterfactual simulations without active contact tracing and quarantine projected a median of 2,432 infections (90% prediction interval: 1,867-3,185), approximately 138% more than the model-predicted baseline (1,020 cases). Model fidelity required a highly localized between-school contact structure; broader contact assumptions substantially overpredicted both case counts and geographic spread. Simulated regional introductions confirmed that outbreaks arose only near connected clusters of undervaccination, while well-vaccinated areas remained contained. Model versions deployed prospectively at weeks 9, 17, and 34 of the response informed resource allocation decisions and provided operational evidence for the effectiveness of contact tracing and quarantine strategies. Critically, the model reproduced geographic spread, number of affected schools, and breakthrough infection counts not used in fitting, validating its structural fidelity and establishing a replicable framework for jurisdictions maintaining school-enrollment and vaccination records.

Frailty assessment is increasingly recommended for risk stratification in chronic disease management, but systematic clinical screening for frailty remains difficult to implement because it is resource-intensive. We investigated whether routinely collected clinical measures could be repurposed to prioritize adults with chronic diseases for clinical frailty assessment. We conducted a prediction study using UK Biobank data. A stacked ensemble model was developed using 12 routinely and low cost measured clinical variables to predict frailty. Gain metrics were used to quantify the proportion of frailty cases identified when screening was restricted to individuals with the highest predicted risk of frailty. We compared health profiles (mortality, polypharmacy, self-rated health, falls) of frail and non-frail individuals across different levels of predicted risk. The robustness of this screening strategy was evaluated across 3,284 modelling settings. Among 281,729 participants (mean age 57.9; 54.7% women) with at least one chronic condition, 5.8% were frail. In the validation sample (N validation=93,910, N training=187,819), screening only the 20% of participants with the highest predicted risk of frailty identified 55.6% of all frailty cases, corresponding to a 2.78-fold improvement compared to random screening. Overall, the model recovered 53.4% of the maximum achievable improvement over random screening. Frailty remained always associated with poor outcomes. Screening performance remained consistent across alternative modelling settings. Routinely collected clinical measures can improve the efficiency of frailty screening among adults living with chronic diseases by prioritizing individuals based on their level of risk. Since these data are already gathered during routine care, this risk-prioritization strategy could support scalable screening without increasing testing burden or clinical workload.

Long-term PM2.5 exposure is associated with increased mortality and is usually represented by annual mean concentration, which does not capture how concentrations are distributed across days. We tested whether the prespecified PM2.5 magnitude-rank index (PMRI), a summary of within-year daily concentration structure, improves mortality prediction beyond the annual mean. Daily modeled PM2.5 estimates were linked to age-adjusted mortality rates for 27,289 county-years from 3,064 U.S. counties during 2003-2011. PMRI is the largest k for which at least k days reached k micrograms per cubic meter. County-grouped 10-fold cross-validation compared models with annual mean alone versus annual mean plus PMRI, with additional geographic, temporal, cause-specific, and specification analyses. Annual mean PM2.5 had the highest standalone predictive performance. Adding PMRI increased county-held-out R-squared from 0.0802 to 0.1391 (increase in R-squared = 0.0589; 95% CI, 0.0499-0.0677) and reduced RMSE and MAE. Improvement persisted across geographic and temporal validations and all six cause-specific mortality outcomes. Within-year PM2.5 exposure structure therefore contains reproducible mortality-predictive information not captured by annual mean concentration.

Health inequalities are often examined through single axes such as class or ethnicity, obscuring how these dimensions interlock. This study applies a longitudinal intersectional approach to examine physical and mental health trajectories across five generations in the UK. We analysed 14 waves of the UK Household Longitudinal Study (2009-2024) using longitudinal Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA). We modelled SF-12 physical and mental health trajectories for 398 intersectional strata defined by gender, ethnicity, occupational class, and generation. Physical health inequalities widened with age, consistent with cumulative disadvantage, while mental health inequalities were more evident among later-born cohorts. The 'Boomer' generation exhibited a relatively flat mental health trajectory across its observed age range. In contrast, 'Generation Z' had lower predicted mental health, particularly among White British women with no work history - a group with poorer predicted trajectories than their South Asian peers. Our study shows how health inequalities are structurally differentiated by cohort and intersectional position. Policy responses should support proportionate universalism, combining broad population-level support with intensified action for groups showing poorer health.

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.

Features of urbanicity have been linked to poor psychiatric health in adulthood, but little is known about their relationship with biological age and cognition earlier in life. Using the Avon Longitudinal Study of Parents and Children (ALSPAC), we tested associations between the built environment and epigenetic ages (EpiAgeAltum, EpiAgeHorvath, EpiAgePACE) (n=2,452), brain age (n=751), and cognition (n=12,199). Analyses were replicated in Generation R and triangulated in the Millennium Cohort Study. The built environment was not associated with epigenetic ages ({beta}Altum range = 0.013-0.015, p range = .466-.689; {beta}Horvath range = -0.011-0.023, p range = .182-.728; {beta}PACE range = -0.045-0.013, p range = .131-.524) or brain age ({beta} range = 0.022-0.028, p range = .337-.429). A negative association with cognition was observed in Generation R. We provide no consistent evidence that childhood built environment exposure relates to biological age and cognition in adolescence, suggesting these relationships may emerge in later life.