Environmental Health Perspectives

O_LIHeavy metals are pervasive environmental contaminants that can impair the health of organisms globally. As the largest anthropogenic source of the potent neurotoxin mercury (Hg), gold mining has amplified these threats throughout the tropics. Consequently, there is a mounting need to monitor Hg contamination of the richest biological communities on Earth. Venous whole blood provides a reliable, nonlethal measurement of recent dietary and site-derived contamination, but collecting and cold-storing samples can be impractical in field conditions. C_LIO_LITo overcome these challenges, we developed and evaluated a method to assay Hg exposure in vascular organisms by measuring the volume of dried blood spots (DBS) in the field, which can be stored at ambient temperatures until analysis. We explored the methods precision and accuracy in estimating whole blood Hg concentrations by collecting paired whole blood and DBS aliquots from birds (n = 527 individuals, 140 species) along a trophic gradient (i.e., granivores to piscivores) in Belize and Peru. C_LIO_LIUsing a Bayesian linear mixed-effects model, we found a highly precise and unbiased relationship between DBS and whole blood total Hg concentrations that was centered at perfect unity (R2 = 0.99; {beta} = 1.00 {+/-} 0.03; 95% CrI: 0.95-1.05). Agreement between individual paired aliquots was more variable, in which approximately 12% of DBS containing at least 1 ng THg differed from whole blood by more than {+/-}20%. However, DBS accuracy increased at higher THg concentrations, suggesting that disagreement at low concentrations is an expected consequence of higher measurement error near the analytical limit of detection of our instruments. C_LIO_LICompared to whole-blood collection and analysis workflows, DBS offer substantial logistical advantages by eliminating cold-chain dependence and reducing transport burden, laboratory handling time, and overall operational costs. Consequently, volume-measured DBS provide a practical and highly reliable alternative for monitoring Hg contamination in both humans and wildlife, particularly for ecological and population-level applications in remote and resource-limited environments. C_LI

Background: Endocrine-disrupting chemicals (EDCs) in consumer products are ubiquitously detected in human biospecimens, yet most epidemiological studies examine single chemicals rather than real-world co-exposures. We evaluated associations between a mixture of seven urinary chemical biomarkers and systemic inflammation. Methods: Survey-weighted log-log regression models adjusted for age, sex, race/ethnicity, poverty-income ratio, and survey cycle were conducted with Benjamini-Hochberg FDR correction (primary analysis, N=4,864). A sensitivity analysis additionally adjusted for body mass index and smoking status (N=4,494). Results: In the primary analysis, 5 of 7 chemicals showed significant associations after FDR correction: ethylparaben ({beta} = -0.056, FDR P < .001), propylparaben ({beta} = -0.026, FDR P = .007), bisphenol A ({beta} = +0.052, FDR P = .005), monoethyl phthalate ({beta} = +0.043, FDR P = .002), and monocyclohexyl phthalate ({beta} = +0.215, FDR P = .007). The WQS mixture index was significantly associated with CRP ({beta} = +0.056, 95% CI [0.031, 0.081], P < .001), with monocyclohexyl phthalate carrying the largest mixture weight (0.342). In the BMI- and smoking-adjusted sensitivity analysis, associations attenuated to null for all chemicals, though MCP preserved direction ({beta} = +0.129) and the WQS mixture direction was maintained ({beta} = +0.018). Two multiple imputation sensitivity analyses confirmed that monocyclohexyl phthalate was the only chemical to maintain a positive direction across all four analytical specifications (primary complete-case, BMI-adjusted complete-case, primary-aligned imputation, and BMI-adjusted imputation), reaching statistical significance in three of four specifications and providing convergent evidence of a robust MCP-inflammation association. Conclusions: The chemical mixture showed a significant collective association with systemic inflammation, consistent with a cumulative pro-inflammatory burden from co-exposure to multiple consumer product chemicals. These findings suggest that regulatory approaches should shift from single-chemical to mixture-based risk assessment frameworks for consumer product safety.

Environmental exposures are increasingly examined in relation to mental health, yet large-scale epidemiological analyses remain constrained by fragmented geospatial data, heterogeneous spatial and temporal resolutions, and privacy-preserving linkage requirements, limiting systematic investigation of multiple environmental domains at the population level. We present environMAP, a harmonised set of analysis-ready environmental exposure layers derived from open, global sources. environMAP spans the built environment, green and blue spaces, light exposure (solar radiation and night-time light), terrain, weather and extremes, and air pollution. We document data provenance, spatial buffers, preprocessing, projection alignment, and metadata, and provide a reproducible workflow for privacy-preserving linkage to cohort residential locations. To demonstrate utility, we linked environMAP to >200,000 adults in the German National Cohort (NAKO) and summarised self-reported lifetime doctor-diagnosed depression across exposure gradients using sex-stratified descriptive analyses. Gradients were interpretable and broadly consistent with prior evidence, supporting feasibility, scalability, and hypothesis generation. The framework is adaptable to other outcomes, cohorts, and regions.

Wastewater-based surveillance (WBS) is increasingly used to monitor infectious disease dynamics, yet most evaluations focus on correlation or forecasting - neither of which directly assesses whether wastewater signals can identify the epidemiological events most relevant to public health decision-making. We argue that outbreak onset and epidemic peak detection are the operationally critical use cases of WBS, requiring a fundamentally different evaluation framework. We introduce a classification-based framework that treats WBS as an event-detection problem, defining outbreaks and peaks as discrete events, establishing detection intervals to account for timing uncertainty, and incorporating censoring and data completeness criteria for valid comparisons against imperfect clinical reference outcomes. Within this framework, we apply a Bayesian exponential growth model for outbreak detection - benchmarked against a standard reproductive number (Rt)-based method - and a rule-based algorithm for peak detection, evaluating performance via sensitivity and positive predictive value (PPV). Applied to county-level SARS-CoV-2 wastewater data from 281 U.S. counties (Biobot, 2021-2024), the exponential growth approach substantially outperforms the Rt-based baseline: sensitivity 0.82 and PPV 0.64 versus sensitivity 0.58 and PPV 0.19 for the best-performing Rt variant. Peak detection achieves sensitivity 0.84 and PPV 0.70 at the county level. Both peak and outbreak detection achieve strong and consistent performance against hospitalizations and deaths at the state level. Spatial aggregation yields a statistically significant improvement in peak detection PPV against a curated reference standard ($p < 0.001$), while outbreak detection improvements under aggregation are directionally consistent but not statistically significant. Wastewater leads case-defined outbreaks by 4-6 days but minimally leads epidemic peaks, consistent with wastewater approximating prevalence rather than incidence. These findings demonstrate that wastewater signals can reliably detect outbreak onset and epidemic peaks across spatial scales and clinical outcomes, and that the choice of detection method matters substantially in practice. The classification framework developed here provides a reusable and principled tool for evaluating any surveillance signal as an event-detection system, with direct relevance to how WBS is actually used in public health decision-making.

Background: Climate mitigation policies can lower air pollutant concentrations and deliver substantial health co-benefits. The French Ecological Transition Agency (ADEME) proposed four contrasting Transitions 2050 net-zero scenarios. We quantified mortality, morbidity, and health-economic co-benefits from projected PM2.5 and NO2 reductions across all four scenarios in continental France. Methods: Emission projections were input to the CHIMERE chemistry-transport model to estimate PM2.5 and NO2 concentrations for 2030 and 2050. Health impacts were assessed using disease-specific cessation-lag assumptions relative to 2019, covering premature mortality, morbidity, DALYs, and economic benefits across nine outcomes (hypertension, lung cancer, ischaemic heart disease, stroke, COPD, type-2 diabetes, acute lower respiratory infections, and asthma in children and adults). Findings: Population exposure is projected to decline by about 40% for PM2.5 and 70% for NO2 by 2050, with health gains remaining substantial and broadly equivalent across all four scenarios and modest differences between sufficiency-oriented and technology-driven pathways. Under delayed-impact assumptions, avoided premature deaths ranged from 21,300 to 22,100 for PM2.5 and 24,500 to 26,200 for NO2. Morbidity and disability-adjusted life year (DALY) reductions, as well as economic savings, spanned similarly; total avoided morbidity cases were 84,000-88,000, direct medical cost reductions were e1.0-1.1 billion/year, and intangible cost savings of e41-43 billion and e36-39 billion, respectively. Interpretation: Health co-benefits are substantial, consistent across contrasting scenarios, and increase markedly from 2030 to 2050. Explicitly incorporating these co-benefits into climate policy appraisals may strengthen the case for ambitious mitigation and improve decision-maker acceptability.

Wastewater-based epidemiology has emerged as a powerful complement to clinical surveillance for monitoring infectious disease dynamics. However, most existing approaches either treat wastewater sites in isolation, overlooking spatial dependencies, and often fail to account for variability in data quality, limiting their ability to generate reliable predictions of healthcare demand. Here we present a spatial Bayesian renewal framework that integrates wastewater surveillance with mobility-informed spatial interactions while incorporating reliability-weighted wastewater signals. We apply the framework to three major respiratory pathogens, i.e., SARS-CoV-2, influenza, and respiratory syncytial virus (RSV), using wastewater and hospital data from counties in South Carolina. Across rolling four-week forecasts, the spatial framework consistently outperforms non-spatial approaches and remains robust even in counties lacking direct wastewater or hospitalization observations. Importantly, we show that county-level forecasts can be translated into facility-level predictions, enabling localized assessment of healthcare demand. These forecasts provide actionable early-warning signals to support hospital capacity planning, staffing decisions, and resource allocation. Together, this work establishes a scalable digital surveillance framework that integrates heterogeneous data sources for enabling more reliable infectious disease forecasting and supporting public health decision-making in underserved and data-limited settings.

Inflammatory bowel diseases (IBD) affect millions of patients worldwide and impair quality of life. Although genetic and environmental factors are known to disrupt the gastrointestinal (GI) epithelial barrier and increase susceptibility to IBD, the precise contribution of specific environmental exposures remains unclear. Per- and polyfluoroalkyl substances (PFAS), or "forever chemicals," are widely used in consumer products and contaminate food and water sources, resulting in chronic oral exposure worldwide. Perfluorooctanoic acid (PFOA), a common PFAS, has been epidemiologically associated with the development of IBD, particularly in older adults. Here, we assessed the effects of oral PFOA exposure on the GI tract, liver, and susceptibility to colitis. C57BL/6 mice were exposed to PFOA (0.1 mg/kg or 1.0 mg/kg) beginning at weaning (post-natal day [P]21) for a time course of 4 or 8 weeks. GI physiology/pathology (Ussing chambers; histology), expression of pro-inflammatory cytokines (qPCR), microbiota composition (16S sequencing), bile acids production (qPCR; LC/MS), and liver pathology (histology) were assessed. Colitis susceptibility was evaluated in genetically predisposed (IL10 knockout) mice, and in induced (dextran sodium sulfate [DSS]) mouse models following PFOA exposure (8 weeks at 1.0 mg/kg). Oral PFOA exposure increased intestinal permeability, mildly increased cytokine expression, altered gut microbiota composition, disrupted liver and serum bile acids, and caused hepatic hypertrophy at higher doses and longer exposure. Although PFOA did not increase disease susceptibility in genetically predisposed Il10 KO mice, it significantly worsened DSS-induced colitis, but only in male mice. Together, these findings demonstrate that early-life PFOA exposure disrupts the gut-liver axis and may contribute to colitis development in a sex dependent manner.

We examined associations between a 15-component urinary biomarker mixture related to consumer product chemical exposure and wearable-derived circadian light exposure patterns in U.S. adults. Using National Health and Nutrition Examination Survey (NHANES) 2011-2014, we studied adults aged 20 years or older with valid wrist-worn ambient light data and urinary chemical biomarkers (N = 1,666). Eight circadian light metrics were derived from hour-level ActiGraph GT3X+ data. A standardized chemical burden index and quantile g-computation were used in survey-weighted linear regression adjusted for age, sex, race/ethnicity, poverty-income ratio, education, body mass index, cotinine, sleep duration, and season. Higher chemical burden was associated with greater morning light ({beta} = 0.54; 95% confidence interval [CI]: 0.14, 0.94), greater nighttime light ({beta} = 0.55; 95% CI: 0.21, 0.89), and earlier light centroid timing ({beta} = -1.37 hours; 95% CI: -2.14, -0.59) after false discovery rate (FDR) correction. Quantile g-computation confirmed these three outcomes. No sex modification was observed (all interaction P > .23). Higher consumer product chemical mixture burden co-occurred with an early-shifted circadian light exposure profile, consistent with shared behavioral, occupational, and environmental determinants.

Background Bacteriological contamination of drinking water remains a major public health burden in sub-Saharan Africa, yet the full contamination chain from source to household has rarely been quantified at national scale. This study analyses water quality at both levels using the 2021 Cote d'Ivoire Demographic and Health Survey (DHS-CI 2021). Methods Cross-sectional secondary analysis of DHS-CI 2021 data. Households with paired bacteriological tests at the source (SH3227) and at the household (SH3225) were included (n = 2,541 for determinants; n = 2,528 for chain analysis). Contamination was defined as >0 CFU/100 ml. Determinants of source contamination were assessed by weighted logistic regression accounting for complex survey design. The contamination chain was described across four categories: safe throughout, recontaminated during transport/storage, decontaminated at home, and contaminated throughout. Results Weighted prevalence of source contamination was 63.6% [95% CI: 60.7-66.5%] and 77.0% [74.1-79.9%] at the household. Only 15.0% of households had safe water throughout the chain; 21.2% showed domestic recontamination and 60.8% consumed water contaminated at both levels. Key determinants of source contamination were use of an unimproved source (aOR = 8.15; 95% CI: 4.54-14.66), administrative region, travel time [&le;]30 minutes (aOR = 1.92; 95% CI: 1.41-2.62), and higher wealth quintiles (protective; aOR = 0.25 for richest). Model discrimination was good (AUC = 0.809). Conclusions The vast majority of Ivorian households consume bacteriologically unsafe water, with domestic recontamination representing a distinct and significant degradation pathway even among users of improved sources. Dual interventions targeting source protection and safe household water storage are urgently needed to advance progress toward SDG 6 in Cote d'Ivoire.

Introduction Air pollution is a leading driver of cardiovascular disease with a growing body of literature implicating this in worse glucose homeostasis. Increases in fine particulate matter air pollution (PM2.5) are associated with increased blood glucose and hemoglobin A1c across the glycemic spectrum from normoglycemia to prediabetes to all forms of diabetes. Despite strong evidence for positive associations of PM2.5 with dysglycemia, it remains unknown if reducing air pollution exposure through air filtration can effect improvements in glucose. This study aims to test the hypothesis that short-term, in-home air pollution reduction using high efficiency particulate air (HEPA) filtration will improve blood sugar in adults with prediabetes. Methods and analysis This trial is a randomized, double-blind, sham-controlled trial of the effects of lowering air pollution exposure using HEPA filtration on cardiometabolic health in adults with prediabetes living in the New York City area. Participants will be randomly assigned to use bedroom air cleaners, or sham air cleaners, while measuring PM2.5 continuously for 1 month. The primary outcomes will be continuous glucose monitoring metrics measured before and after HEPA air filtration. Exploratory outcomes will include insulin resistance measures, serum biomarkers and transcriptomics measured before and after HEPA intervention. We will quantify effects of HEPA filtration with models using treatment arm (true versus sham filtration) as the independent variable. Secondary analyses will model continuous measures of PM2.5 as the independent variable. Ethics and Dissemination This study has undergone peer review; and the work was supported by Grant 2023-0214 from the Doris Duke Foundation, who had no other role in study design or implementation. The study was registered in ClinicalTrials.gov (NCT05994937) prior to recruitment. Clinical Trials Clinical Trials NCT05994937; https://clinicaltrials.gov/study/NCT05994937

Ozone (O3)-driven pulmonary inflammation is partly regulated by damage associated molecular patterns (DAMPs) binding to scavenging receptors (SRs). However, how SRs and DAMPs regulate O3-induced pulmonary inflammation remains incompletely understood. CD163 is a SR responsible for clearing cell free hemoglobin (CFH), a DAMP which accumulates during acute pulmonary injury and is associated with worsening respiratory outcomes. We hypothesized that increased CD163 is necessary for reducing CFH levels and resolving O3-induced pulmonary injury. To test this hypothesis, we defined CD163 and CFH responses to O3 exposure in C57BL/6N (WT) and CD163 deficient (Cd163-/-) mice, as well as in human bronchoalveolar lavage fluid (BALF). In WT mice, lung Cd163 expression was significantly increased by O3 during peak inflammation and declined 24 hours post exposure. Human exposure studies revealed a diversity of Cd163 expression and a reduction of CFH following O3 exposure, suggesting regulation of this pathway in humans. When compared to WT mice, Cd163-/- mice had augmented O3-induced pulmonary injury, inflammation, and oxidative stress. Further, the antioxidant EUK-134 did not reduce O3-induced pulmonary oxidative stress in Cd163-/- mice, suggesting a role for CD163 in the pulmonary response to oxidative insults. Furthermore, compared to WT controls, Cd163-/- mice receiving an oropharyngeal aspiration of CFH had a significant increase in airspace inflammation. Combined, these findings suggest that CD163 mediated clearance of CFH is involved in resolving O3-induced pulmonary injury, inflammation, and oxidative stress. New & NoteworthyOzone (O3) is known to induce damage associated molecular patterns (DAMPs) which drive lung inflammation. The scavenging receptor, CD163, binds and clears the DAMP cell free hemoglobin (CFH), which accumulates during sterile lung injury. Our findings indicate that O3 exposure alters CD163 expression in the lung and that mice lacking Cd163 expression have more lung inflammation. Our data indicate that CD163 serves a protective role in response to acute O3 exposure perhaps through CFH clearance.

Adverse outcome pathways (AOPs) describe stressor non-specific sequences of events between a first molecular trigger (molecular initiating event, MIE), causally linked key events (KEs), and an adverse outcome (AO). AOPs are intended to aid in chemical toxicity testing as a new approach methodology. However, commonly used AOP development methods depend on manual curation, which is labor intensive. As a result, there are still relatively few AOPs and a huge number of toxicity mechanisms and possible adverse outcomes remain undescribed. Therefore, systematic and high-throughput approaches to predict new AOPs are needed. Here, we developed and implemented a data integration-based framework to generate new candidate AOPs using insecticides and Parkinson Disease as a proof of concept. We integrated and statistically linked disconnected databases (e.g., Comparative Toxicogenomics Database, Human Protein Atlas, and Gene Ontology) to form MIE - KE (cell level) - KE (tissue level) - AO candidate AOPs. Through this systematic process, we generated 562,117 candidate AOPs, which we then scored using a weight of evidence (WoE) approach and prioritized 12,756 AOPs with a WoE >0.5. Through random sampling of 100 prioritized AOPs, we found 70% had external literature supporting their biological plausibility, and only 15% represented identifiably implausible associations. The prioritized AOPs describe varied mechanisms of toxicity related to e.g., MAPK, PTEN, and FGFR signaling pathways, with "increases phosphorylation of MAPK1" as the most frequent MIE. Our AOP generating approach yields consistently structured AOPs and can complement existing and emerging development methods to expand AOP coverage across different stressors and outcomes.

IntroductionIt is now widely recognized that environmental exposures can predispose unexposed descendants to disease across multiple generations without inducing genetic mutations. Among the numerous unknowns that multigenerational effects still hold, identifying the most probable windows of susceptibility for multigenerational environmental disease predisposition remains a crucial challenge in preventing such effects. We have proposed that multigenerational environmental effects can be mediated by perturbations in chromatin organization that originate from environmental exposures causing alterations in gamete elements necessary for establishing chromatin organization immediately after fertilization. Based on this hypothesis, it is likely that the period preceding conception serves as a relevant window of susceptibility for multigenerational effects, and that such susceptibility may vary between female and male preconception exposures due to the distinct characteristics of oocytes and sperm. Here, we test this framework using nicotine--a well-established endocrine- and metabolism-disrupting chemical with documented multigenerational effects--and assess whether windows of nicotine cessation prior to conception that span the last stages of gamete maturation mitigate these effects. MethodsWe conducted two asynchronous studies to determine the direct and offspring effects of female preconception exposure (FPE) and male preconception exposure (MPE) to nicotine and nicotine cessation. We exposed C57BL/6J female (FPE) or male (MPE) mice to deionized water (control), continuous nicotine (300 {micro}g/mL), or one of two nicotine cessation windows whose durations did or did not encompass one full round of gamete maturation. Following exposure, we mated exposed mice with unexposed mice of the same age to produce their offspring. We measured the water and food consumption and body weight of exposed mice to determine the efficacy and direct effect of the assayed exposures. We also measured body weight, fasting body weight, fasting glucose, gonadal white adipose tissue and liver weights, and plasma concentrations of twelve metabolic hormones in the offspring of exposed mice to determine the offspring effect of nicotine exposure and its mitigation upon nicotine cessation. We determined the significance of comparisons between nicotine and control groups using the Monte Carlo-Wilcoxon testing framework that we have previously developed. ResultsPreconception nicotine exposure elicited sexually dimorphic metabolic effects in the offspring of exposed mice that differed between FPE and MPE studies. Nicotine cessation mitigated F1 metabolic perturbations only after maternal--not paternal--preconception exposure, and only when the cessation window encompassed one full round of oocyte maturation. ConclusionsThese findings support the hypothesis that preconception exposures perturb offspring metabolism through sex-specific gamete mechanisms and highlight that the efficacy of cessation strategies depends on the parental sex exposed.

Background: Nitrogen dioxide (NO2) is a surrogate for traffic and industrial air pollution associated with adverse respiratory outcomes. Whether elevated NO2 and temperature jointly influence adult-onset asthma (AOA) risk is unclear, especially among subgroups with varying lifestyle and exposure profiles. We investigated further in the prospective All of Us research program. Methods: Among 596,926 U.S. participants who consented to electronic health record release, annual average NO2 concentrations from satellite data were linked to residential locations for 376,535 individuals. We used multivariable Cox regression to estimate associations between NO2, temperature, and incident AOA, adjusting for co-pollutants and potential confounders. We analyzed 4-category cross-classification variables between NO2 (high>75th percentile vs. low<=75th percentile) and maximum or average temperature (high>median vs. low<=median). We also stratified by sex, age, income, and smoking status. Additive interactions were estimated using Relative Excess Risk due to Interaction, Attributable Proportion, and Synergy Index. Results: We identified 10,413 incident AOA cases over an average 4-year follow-up. Participants with the highest categories of NO2 and temperature exposure had significantly higher risk compared to those with the lowest (HRHigh NO2 x High Max. Temp.=1.37, 95%CI:1.26-1.49; HRHigh NO2 x High Average Temp.=1.49, 95%CI:1.38-1.61). The joint association of high NO2 and high maximum temperature was more pronounced among ever-smokers (HR=1.59, 95%CI:1.40-1.81) than never-smokers (HR=1.26, 95%CI:1.13-1.41). Interaction analyses supported super-additive interactions of high NO2 and high average temperature on AOA risk, particularly among ever smokers, lower-income participants, and younger adults. Conclusion: Our findings highlight the respiratory health threat of long-term joint exposure to elevated NO2 and average temperature, particularly among vulnerable subgroups.

Dietary exposure to heavy metals (HMs) via animal-source foods is a critical environmental health pathway. In rapidly industrializing Bangladesh, contamination of the bovine food chain from agricultural feeds and industrial emissions poses an unquantified public health burden. This study evaluated exposure pathways, spatial distribution, mass-transfer dynamics, and health risks of six HMs (Cr, Cu, Cd, Pb, As, and Hg) across the fodder-cattle-human continuum. Samples of beef (n = 76), raw milk (n = 76), commercial cattle feed (n = 40), and fodder (n = 88) were collected from eight sites across industrial and non-industrial zones in Bangladesh and analysed by atomic absorption spectroscopy. Probabilistic Monte Carlo simulations (10,000 iterations) quantified estimated daily intake, target hazard quotients (THQ), cumulative hazard index (HI), and lifetime carcinogenic risk (CR) for adult and pediatric receptors. Copper (Cu) was the dominant contaminant across all matrices, peaking in beef (103.89 {+/-} 15.87 mg/kg) and milk (13.67 {+/-} 1.53 mg/L). Spatial analysis revealed distinct contamination profiles: Pb burden peaked in industrial zones while Cr was elevated in non-industrial sectors. Monte Carlo modelling identified commercial feed as the most efficient transfer vector into beef. Pediatric THQ for Cu significantly exceeded the safety threshold (THQ > 1), and upper-bound lifetime carcinogenic risk from As approached the critical USEPA 10- regulatory ceiling. These findings demonstrate that industrial and agricultural externalities efficiently contaminate the bovine food supply chain in Bangladesh, with copper and arsenic representing the most critical non-carcinogenic and carcinogenic dietary hazards, respectively. Children are disproportionately vulnerable due to lower body weight. The results underscore the need for targeted upstream interventions in commercial feed production and provide evidence to support feed-quality regulation and environmental monitoring in rapidly industrializing settings.

Background: Lung cancer in never-smokers is rising, with a substantial proportion harboring the EGFR mutation. While fine particulate matter (PM2.5) is a recognized risk factor, other intervenable pollutants and built environmental factors remain unknown. Objectives: To identify urban characteristics associated with EGFR-mutant (vs. wild-type) lung cancer using high-resolution spatiotemporal data. Methods: We analyzed 2,699 lung cancer patients with documented EGFR status treated at a high-volume academic medical center in New York City. Patient residential addresses were linked to high-resolution (300m x 300m) 5-year cumulative exposures to 3 air pollutants and 26 urban features. We developed Light Gradient Boosting Machine (LightGBM) models to classify EGFR status, comparing a basic clinical model with established predictors (Asian, female, never-smoking status, and adenocarcinoma histology) to an extended model with additional urban factors. Predictive performance was assessed based on discrimination (AUC). Results: We included 2,699 patients, of whom 54.1% were female and 25.8% self-identified as Asian, 11.2% as Black, and 7.4% as Hispanic; and 29% had EGFR-mutated cancer. The extended model showed modest improvements in discrimination (AUC: 0.775 [95% CI, 0.739-0.809] vs. 0.768 [0.723-0.811]), compared to the clinical model. Newly identified factors for EGFR-mutant status included black carbon (BC), nitrogen dioxide (NO2), proximity to airports, reduced access to public transportation, elevated noise levels, and lead exposure. Conclusions: Traffic-related pollutants (BC, NO2) from diesel engines and motor vehicles, and proximity to airports, were among the novel spatiotemporal features associated with EGFR-mutant lung cancer. These results may inform policy interventions.

Background: Mixture epidemiology deploys sophisticated estimators, Bayesian kernel machine regression with causal mediation analysis (BKMR-CMA), quantile G-computation (QGC), and parametric G-computation, alongside conventional regression. Comparative evaluations have assumed additive, non-mediated data-generating processes, leaving conditions under which estimator choice determines causal validity uncharacterized. Methods: We developed a simulation framework using military-relevant exposure distributions (metals, per- and polyfluoroalkyl substances [PFAS], polychlorinated biphenyls [PCBs]) and allostatic load (AL) across three deployment tiers, with parameters drawn from military occupational health and contamination literature. Four data-generating processes were specified as directed acyclic graphs: direct effects with confounding (M1), full mediation through AL (M2), synergistic AL-exposure interaction (M3), and collider structure (M4). We evaluated ordinary least squares (OLS), QGC, G-computation, and BKMR-CMA on bias, root mean squared error, and 95% confidence interval coverage across 500 Monte Carlo replications at n = 500 and n = 1,000. Results: No estimator dominated across all mechanisms. Under M1, OLS and G-computation produced near-identical modest positive bias; BKMR-CMA achieved lower root mean squared error through kernel shrinkage. Under M2, BKMR-CMA exhibited severe positive bias for AL (mean bias = +0.579 SD units; coverage = 32.8%). Under M3, BKMR-CMA was the only estimator achieving nominal 95% coverage for AL (95.2%), while regression-based approaches fell to 83.6%. Under M4, G-computation produced persistent bias and near-zero coverage for lead, reflecting structural non-identification. Conclusions: Estimator validity is fundamentally mechanism-dependent. Researchers should base estimator choice on explicit causal assumptions about whether AL functions as confounder, mediator, moderator, or collider, particularly in military and occupational cohorts. We provide a mechanism-to-estimator mapping for applied researchers.

Background: In the absence of high-resolution response data, exposure-response modelling often relies on aggregated low-frequency exposure data, leading to loss of high-resolution information. Mixed Data Sampling (MIDAS) from econometrics offers an alternative but is limited due to its inability to make high-resolution predictions, inflexible likelihoods and penalised nonlinear functions, and limited visualization options. We propose a mixed-frequency Distributed Lag Non-linear Model (mf-DLNM) which can eliminate the need to aggregate exposure data in environmental epidemiology and provide high resolution predictions for time series studies. Methods: We evaluated the inference and predictive performance of the mf-DLNM. To evaluate its ability to estimate exposure-response relationships, we applied mf-DLNM and same-frequency (sf)-DLNM using data from the West Midlands, UK. Additionally, we compared the predictive performance of mf-DLNM with sf-DLNM and MIDAS across nine regions of England. As MIDAS cannot predict at the resolution of the predictor (daily), we compared the predictive performance of mf-DLNM and MIDAS at weekly resolution. To test the model's ability to predict high temporal resolution risk (daily), we compared sf-DLNM (with access to daily mortality counts) with mf-DLNM (with access only to weekly mortality counts). Results: In the West Midlands example, mf-DLNM performed comparably to sf-DLNM in estimating daily risk of temperature on respiratory mortality. Furthermore, mf-DLNM and MIDAS exhibited similar performance for weekly predictions. For high-resolution predictions, mf-DLNM and sf-DLNM showed nearly similar performance, despite mf-DLNM having access only to low-resolution response data. Conclusion: This mixed-frequency approach in environmental epidemiology overcomes the limitations of predicting health risks using aggregated exposure data and provides estimates of high-resolution outcomes in the absence of high-frequency health outcome datasets.

Lead is a toxic metal ubiquitous in our environment. While dramatic reductions in lead sources have paralleled equivalent decreases in lead-poisoning rates, chronic lead exposure remains a critical public health concern. Childhood lead exposure (at its lowest levels) is liked to changes in cognitive development but less is known about lead's effects on children's brain structure, especially as a result of in utero exposure. We measured prenatal and early-postnatal lead exposure in shed deciduous teeth of 448 9- and 10-year-old children (from 20 United States cities) and linked those lead levels to childhood brain structure, cognition/behavior, and neighborhood- and family-level socioeconomic characteristics. Here we show negative associations between tooth-lead levels and the thickness of the brain's cortex, particularly in regions linked to language processing. With increasing tooth-lead levels, children of lower-income (versus higher-income) families showed steeper declines in receptive vocabulary. Caregiver-reported behavioral problems exhibited similar associations. With in utero exposure linked to adverse neurodevelopmental outcomes (well before lead exposure and its risks are evaluated by healthcare professionals), prenatal screening of maternal lead levels/exposure, coupled with recommended strategies to reduce its placental transmission, may help reduce lead's effects on future generations.

Background Foodborne diseases cause substantial global morbidity and mortality, yet remain largely unattended. To support countries to address this public health concern, the World Health Assembly Resolution 73.5 called for strengthening global food safety efforts and led to the development of the WHO Global Strategy for Food Safety 2022-2030, adopted at the 75th WHA (2022). To this end, the World Health Organization (WHO) reconvened the Foodborne Disease Burden Epidemiology Reference Group (FERG) to advise and support the work to generate updated global, regional, and national estimates of the foodborne disease burden for the reference period 2000-2021. Methods We developed an incidence-based framework expanding coverage to 42 foodborne hazards. Standardized systematic reviews, Global Health Estimates and Global Burden of Disease envelopes, and United Nations population data informed the evidence base. Missing epidemiological data were imputed using Bayesian hierarchical meta-regression models. Disease models mapped acute and chronic health outcomes, applying updated disability weights, life tables, and probabilistic Monte Carlo calculations to estimate incidence, mortality, Years Lived with Disability, Years of Life Lost and Disability-Adjusted Life Years for all 194 WHO Member States. Transparency and analysis reproducibility were ensured through availed open-source R packages and standardized workflows. Results The computational framework provides annual, country-level estimates with improved internal consistency and an expanded hazard scope compared with the WHO 2015 edition. Advances include refined modelling, enhanced uncertainty propagation, and broader inclusion of microbial, parasitic, and chemical hazards. Persistent data gaps---especially in high-burden regions---were filled through extensive imputation. Conclusions The computational framework for the WHO 2026 edition delivers the most comprehensive and transparent assessment of the global burden of foodborne diseases to date. Despite remaining limitations, it enables routine monitoring, supports evaluation of global food safety efforts, and highlights priorities for strengthening national data systems.