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

Exposure to high ambient temperature is responsible for more than 11,000 deaths and over 230,000 disability-adjusted life-years in the United States each year. However, which individuals and populations are most at risk, and why, is still not well understood. In 2015, a subset of "Z" diagnosis codes (or "Z-codes") were introduced as a standardized option for healthcare providers to document the social needs and conditions of their patients. To assess heat-related risk across social determinants of health (SDoH), we leverage these codes using a dataset of patient-level emergency department (ED) visits from seven US states. Using a bi-directional, time-stratified, case-crossover design and conditional logistic regression, we compared hospital encounters for seven different health outcomes with SDoH Z-codes at discharge to a reference group matched on age, sex, race, ethnicity, year and hospital. We investigated the following Z-code domains: inadequate housing (Z59.0, Z59.1), poverty-related (Z56.0, Z59.5-Z59.7), living alone (Z60.2), institutional living (Z65.1, Z59.3), and other problems with the social environment (other Z60 sub-codes). We calculated cumulative odds ratios (ORs) for a 3-day lag change in temperature from the 95th to 50th percentile, using ZIP code-specific temperature percentiles. Among 60,557,958 ED visits with available demographic and meteorological data, 461,468 (0.8%) included a SDoH Z-code. Across temperature metrics and outcomes, patients with SDoH Z-codes consistently showed higher associations with heat than the matched reference group without SDoH Z-codes. The largest difference was for acute kidney injury, with a ratio of ORs of 1.21 (1.10,1.33) for daily mean temperature. Notable subgroup findings included elevated kidney-related risks in patients with inadequate housing or poverty-related SDoH, increased mental health risks among those living alone, and elevated cardiovascular risks in people with other problems related to the social environment.

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.

BackgroundRising global temperatures and eutrophication are increasing the intensity and frequency of cyanobacterial harmful algal blooms that release toxins including microcystin-LR (MC-LR). MC-LR inhibits protein phosphatases in the human liver and brain, but its accumulation in the placenta is unclear. Placental transporter expression varies across pregnancy and is influenced by physiological cues, such as low oxygen concentrations which activate HIF1A, and trophoblast cell fusion forming syncytiotrophoblasts that engage CREB-driven transcription. This study examined whether MC-LR accumulates in placental cells, which transporters mediate uptake, and how these transporters are regulated by HIF1A and CREB. MethodsIntracellular accumulation of MC-LR (0.1-10 {micro}M, 3 hour) was measured in human cytotrophoblasts (JAR, BeWo) and extravillous trophoblasts (HTR-8/SVneo) by western blotting for MC-LR-adducted proteins. Organic anion transporting polypeptide (OATP) involvement was tested using cyclosporin A (10 {micro}M), an OATP inhibitor, before exposure to the OATP substrate or MC-LR. Cells were also cultured under 3%, 8%, or 20% O2 to induce hypoxic responses or treated with forskolin (a potent intracellular cAMP inducer) to stimulate cell fusion before MC-LR exposure. ResultsMC-LR accumulated in all three placenta cell lines in a concentration-dependent manner. Cyclosporin A reduced MC-LR uptake by 57% in JAR cells, confirming OATP-mediated transport. Low O2 increased OATP4A1 expression and function but reduced protein phosphatase expression, decreasing MC-LR-bound proteins by 52-72%. Forskolin increased OATP4A1 expression and enhanced MC-LR uptake >2.5-fold. ConclusionMC-LR enters placental trophoblasts via active OATP transport, likely OATP4A1, and uptake increases under hypoxia and trophoblast fusion.

IntroductionDrowning risk begins with water exposure, yet population-water relationships have rarely been quantified at scale using environmental measures. This study explored whether satellite-derived data was associated with subnational drowning mortality and whether associations differed by country income level. MethodsWe linked Global Burden of Disease (GBD 2021) age-standardised drowning mortality rates to satellite-derived exposures for 212 subnational regions across 12 countries (2006-2021; 3,392 region-years). Exposures were extracted via Google Earth Engine and standardised. Gamma-log generalised linear mixed models included region random intercepts and year fixed effects. Income-stratified models were estimated separately. Supplementary models assessed maritime vessel activity. ResultsNear-water population percentage was the strongest correlate of drowning (IRR 1.40; 95% CI 1.33-1.47). Permanent water coverage was protective (IRR 0.80; 0.73-0.88), as were nighttime lights (IRR 0.96; 0.95-0.97) and hot days [&ge;]30{degrees}C (IRR 0.95; 0.92-0.99). Mean temperature (IRR 1.17; 1.11-1.23) and precipitation (IRR 1.03; 1.01-1.04) were positively associated. Near-water effects were consistent across income strata (LIC 1.25; MIC 1.31; HIC 1.24), while other predictors showed weak or inconsistent within-strata associations. Vessel activity was modestly associated with drowning in Global Fishing Watch models (IRR 1.05; 1.01-1.09) but not in Synthetic Aperture Radar models. DiscussionSatellite-derived indicators can characterise drowning risk at scale, with population proximity to water emerging as a robust cross-context correlate. Protective associations for permanent water suggest landscape configuration may shape risk beyond proximity alone, highlighting geospatial datas value for targeting prevention where surveillance is limited.

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.

Background Ambient air pollution is a leading global health risk and disproportionately affects populations of Low- and Middle-Income Countries (LMICs). In 2021, WHO revised its Air Quality Guidelines (AQG), lowering recommended annual limits for Particulate Matter 2.5 (PM2.5) and Nitrogen Dioxide (NO2). We estimated the potential health and economic impacts of achieving WHO Interim Target 3 (IT3) and AQG concentrations across LMICs. Methods We conducted a health impact assessment across 136 LMICs to quantify one-year changes in all-cause and cause-specific mortality (chronic obstructive pulmonary disease [COPD], ischaemic heart disease [IHD], and stroke) and disease incidence (COPD, dementia, IHD, and stroke) under WHO IT3 and AQG counterfactual scenarios for PM2.5 and NO2. Concentration-response functions were applied at 1km x 1km resolution. Economic welfare impacts of mortality risk reductions were estimated using country-adjusted values of a statistical life (VSL, Int$ PPP-adjusted 2021). Direct medical and productivity-related costs associated with incident cases were estimated using a cost-of-illness (COI) framework. Uncertainty intervals (UI) reflect uncertainty in concentration-response functions. Results Attainment of WHO IT3 and AQG concentrations for PM2.5 was associated with an estimated 16.04% reduction (6.58million, UI: 6.10-7.07million) and 22.97% reduction (9.43million, UI: 8.75-10.11million) in annual deaths, respectively. Corresponding VSL-based estimates of deaths averted were Int$5.5 trillion (7.0% of aggregate LMIC GDP) and Int$8.4 trillion (10.6% of GDP), respectively. For NO2, IT3 and AQG scenarios were associated with estimated reductions of approximately 1.06% (approximately 435,000 deaths, UI: 388,000-483,000) and 2.79% (435,000 deaths; UI: 388,000-483,000), yielding gains of Int$0.6 trillion (0.7% of GDP) and Int$1.5 trillion (1.9% of GDP). Disease-specific mortality reductions were most prominent for IHD and stroke in Asia and Africa. Under the PM2.5 AQG scenario, an estimated 2.82million (1.67-2.97) COPD, 1.10million (0.83-1.37) dementia, 7.3million (6.41-8.19) IHD, and 2.3million (2.19-2.41) stroke cases could be delayed or averted in one year. Associated reductions in direct medical and productivity-related costs were greatest for IHD, COPD, and stroke. NO2-related morbidity reductions were smaller across all outcomes. All estimates represent one-year changes in risk relative to counterfactual exposure and may reflect delayed rather than permanently avoided events. Discussion Achieving both WHO IT3 and AQG values in LMICs could yield substantial reductions in premature mortality and disease incidence, particularly for cardiovascular and respiratory conditions, alongside large, monetised welfare gains from reduced mortality risk. These findings underscore the considerable societal value of air quality improvements and support accelerated action toward meeting WHO guideline levels in regions bearing the highest pollution burden.

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.

BackgroundCase-based infectious disease surveillance is subject to ascertainment bias when testing intensity varies across time and population subgroups. We previously developed a regression-based test adjustment methodology using Standardized Testing Ratios (STRs) to correct for differential testing patterns in COVID-19 surveillance data. Wastewater-based surveillance (WWS) measures viral burden in the community independently of diagnostic testing behavior, making it a valuable external validation tool for test-adjusted case estimates. MethodsWe analyzed 111 weeks of paired wastewater and case surveillance data from Ontario, Canada (July 19, 2020 to August 28, 2022). Wastewater SARS-CoV-2 signals from 107 sewersheds across 34 public health units were normalized within sewersheds and aggregated using population-weighted averages. We compared wastewater correlations with crude reported and test-adjusted case counts using Spearman rank correlations, linear regression, and negative binomial distributed lag nonlinear models (DLNM), stratified by epidemic period. ResultsTest-adjusted cases correlated substantially more strongly with wastewater signals than crude reported cases overall (Spearman {rho} = 0.849 vs. 0.679; linear R{superscript 2} = 0.609 vs. 0.191). The advantage of test adjustment was greatest during the Omicron wave, when population-level diagnostic testing contracted sharply following PCR eligibility restrictions ({rho} = 0.924 vs. 0.604; R{superscript 2} = 0.815 vs. 0.470). DLNM incorporating the wastewater signal explained substantially more variance in test-adjusted than crude reported cases (McFadden pseudo-R{superscript 2} 0.898 vs. 0.776), despite similar lag-response structure for both outcomes. ConclusionsWastewater surveillance provides compelling independent validation of a previously described test adjustment methodology for COVID-19 case surveillance. The agreement between wastewater signals and test-adjusted cases was strongest precisely when testing scarcity was most severe, supporting the use of test adjustment to recover accurate infection dynamics from case surveillance data during periods of changing testing access and policy.

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.

The early developmental environment plays a critical role in the etiology of cardiovascular diseases (CVDs), but underlying molecular mechanisms are poorly understood. Exposure to per and polyfluoroalkyl substances (PFAS) are linked to various CVDs, but effects of developmental PFAS exposures on the human heart remain unclear. Using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM), the objective of this study was to investigate the effects of PFAS exposure during cardiac differentiation on gene expression and function of cardiomyocytes. We exposed two hiPSC lines (one male and one female donor) to perfluorooctanoic acid (PFOA), a common and ubiquitous PFAS (0.05, 0.5, 5, 50, 100, 150, 200 M), followed by assessment of cellular number and pluripotency marker expression. PFOA exposure for 72 hours had no significant effects on hiPSC pluripotency, and modest inhibition of proliferation was observed only at the highest concentration. hiPSCs were then differentiated into ventricular cardiomyocytes in the continued presence or absence of PFOA (0, 0.5, 5, 50 M) using an established small molecules protocol. Optical mapping studies using voltage and calcium-sensitive dyes revealed dose and cell line-specific effects of PFOA on cardiomyocyte voltage and calcium dynamics that were still present 10 days after cessation of exposure. Patch clamping studies demonstrated small but significant reductions in repolarizing IKr currents with 5{micro}M PFOA exposure in cardiomyocytes from both donors. Using RNA-seq, we found that exposure to PFOA led to significant changes in transcriptional pathways related to lipids and lipoproteins in the female hiPSC-CM. In the male hiPSC-CM, we observed significant effects on developmental pathways and calcium homeostasis. Thus, we found that environmentally relevant PFOA exposure during cardiomyocyte differentiation affects the electrophysiological properties and transcriptome of hiPSC-CM even after cessation of exposure, with effects that differ by donor cell line. These findings provide direct experimental evidence that transient developmental exposure to PFOA can durably reprogram human cardiomyocyte function, supporting a developmental origin of PFAS-associated cardiovascular risk. Impact StatementThese studies demonstrate that exposure to environmentally relevant levels of PFOA during the differentiation of hiPSCs into cardiomyocytes alters cardiac gene expression and function, with effects that persist beyond cessation of exposure.

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.

IntroductionParticulate Matter (PM2.5) exposure contributes to the global disease burden, yet its monitoring remains sparse and uneven and is limited in many limited ground monitoring network settings. Road-traffic proxy indicators can provide indirect estimates of PM2.5 where measurements are limited but require context-specific validation. We evaluated three PM2.5 road-traffic related proxies:(I) population-Weighted Road Network Density (WRND), (ii) Euclidean (straight line) distance from highways (EH), and (iii) Euclidean distance from main roads (EM). MethodsWe validated proxies using high-resolution outdoor filtered PM2.5 personal exposure measurements collected over 1 year from 343 postpartum participants in The Gambia, Kenya, and Mozambique. Village-level spatial patterns for the PM2.5-proxy relationship were mapped using 5 km hexagonal aggregated tessellations. Proxy-PM2.5 associations were assessed using Spearman correlation, and predictive utility was tested using country-specific and global Random Forest (RF) models (3-fold cross-validation), reporting R2, RMSE, and feature importance ResultsSpatial mapping showed heterogeneous proxy-PM2.5 relationships across and within sites, with elevated PM2.5 occurring in both low- and high-proxy contests. WRND-PM2.5 correlations were weak overall and statistically significant only in Mozambique (r = 0.351; p = 0.005), with non-significant associations in Kenya (r = -0.041; p = 0.673) and The Gambia (r = -0.020; p = 0.909). EH-PM2.5 correlations were positive in The Gambia (r = 0.335; p = 0.053) and Mozambique (r = 0.292; p = 0.020) but negative and significant in Kenya (r = -0.224; p = 0.018).Single-variable RF models performed poorly across all countries (R2 < 0.45) and the Global model (R2=0.42). Combining proxies improved performance in Kenya (R2=0.52; RMSE=31.7{micro}g/m3) and Mozambique (R2=0.60; RMSE=8.9 {micro}g/m3), Global R2=0.46; RMSE=29.1 {micro}g/m3), although in The Gambia, the combined model (R2=0.53; RMSE=37.6 {micro}g/m3) did not exceed the best single-proxy model. ConclusionRoad-network proxies provide context-dependent signals of personal PM2.5 exposure, and predictive performance is strengthened when proxies are combined in a hybrid model.

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

Background: Beaches are popular summertime destinations in Canada. However, they can be affected by specific fecal pollution sources, increasing the risk of recreational water illness. Objectives: This study was conducted to determine the risks of acute gastrointestinal illness (AGI) among Canadian beachgoers and to evaluate the influence of different fecal indicator bacteria (FIB) and other water quality measures on assessing these risks. Methods: In a prospective cohort design, beachgoers were recruited at sites across Canada from 2023 to 2025. Sociodemographic characteristics and exposures were determined through an on-site survey, with a 7-day follow-up survey to determine risks of AGI. Bayesian mixed-effects logistic regression models were fitted to evaluate the effects of an ordinal water contact variable (no contact, minimal contact, body immersion, and swallowed water) on the incident risk of AGI, with an interaction included for water quality indicators. The levels of six FIB and water quality measures were assessed: Escherichia coli, enterococci DNA, three microbial source tracking DNA markers (human HF183/BacR287, human mitochondria, seagull Gull4), and turbidity. Results: A total of 4085 participants were recruited, with 67.6% completing the follow-up survey. The overall incident risk of AGI was 2.6%. Both swallowing water and body immersion increased AGI risks compared to no water contact: median of 20 excess cases (95% Credible Interval [CrI]: 4, 64) and 5 excess cases (95% CrI: 1, 19) of AGI predicted per 1000 beachgoers, respectively. Escherichia coli and seagull DNA marker levels were associated with AGI among those who had water contact, particularly among those who reported swallowing water. Discussion: While the overall burden of AGI due to beach water contact in Canada was low, increased risks are associated with E. coli levels particularly among those who swallow water. This could be related to fecal contamination from seagulls. However, there is substantial uncertainty in the predicted effect sizes.

BackgroundChronic Kidney Disease of unknown etiology is a growing health concern in low-and middle-income countries. While occupational heat stress is recognized as a potential contributor to kidney dysfunction among agricultural workers, the causal relationship between heat stress, core body temperature (Tc), and kidney function remains unclear. MethodsWe conducted an observational study over two harvest seasons in Guatemala, following 148 male sugarcane workers across six months. Heat stress was measured using heat index (HI) and Tc with ingestible telemetric temperature pills. Particulate matter (PM) exposure was measured using personal breathing zone samplers worn during the work shift. We evaluated changes in kidney function using pre-and post-shift estimated glomerular filtration rate (eGFR). We applied G-computation to estimate causal effects and modeled hypothetical policy interventions reducing HI, Tc, and PM exposure, simulating occupational heat reduction strategies. ResultsThe average daily HI was 37.4 {degrees}C (SD: 2.0) with an average Tc increase of 1.16 {degrees}C (SD: 0.48) per shift. Both HI and Tc were associated with declines in eGFR across the work shift. At an HI of 34 {degrees}C, workers experienced an average eGFR decline of about 5 mL/min/1.73 m{superscript 2}, while at 40 {degrees}C the decline exceeded 16 mL/min/1.73 m{superscript 2}. High HI early in the season and elevated Tc later in the season contributed to kidney decline. A simulated intervention reducing HI exposure by 5% improved eGFR change by 1.46 mL/min/1.73 m{superscript 2}. PM exposure did not have a significant impact on eGFR decline. ConclusionReducing workday heat exposure may mitigate acute kidney function decline. These findings support the development of policy interventions aimed at reducing external heat exposure and internal heat strain to protect kidney health. More research is needed to investigate the potential contribution of other environmental factors, including PM exposure.

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.