Environmental Science & Technology

Wastewater monitoring enables non-invasive, population-scale tracking of community infections independent of healthcare-seeking behavior and clinical diagnosis. Metagenomic sequencing extends this capability by enabling broad, pathogen-agnostic detection, genomic characterization, and identification of novel or unexpected threats. Here, we present data from CASPER (the Coalition for Agnostic Sequencing of Pathogens from Environmental Reservoirs), a U.S.-based wastewater metagenomic sequencing network designed for deep, untargeted pathogen monitoring at national scale. This release includes 1,206 samples collected between December 2023 and December 2025 from 27 sites across nine states, covering 13 million people. Deep sequencing (~1 billion read pairs per sample) generated 1.2 trillion read pairs (347 terabases), enabling detection of even rare taxa, with CASPER representing 66% of all untargeted wastewater sequencing data currently available on the NCBI Sequence Read Archive. Virus abundance trends correlate with nationwide wastewater PCR and clinical data for SARS-CoV-2, influenza A, and respiratory syncytial virus, while the pathogen-agnostic approach captures emerging threats, including avian influenza H5N1 during initial dairy cattle outbreaks, West Nile virus, and measles, among hundreds of viral taxa. As the largest publicly available untargeted wastewater sequencing dataset to date, CASPER provides a shared and growing resource for pathogen surveillance and microbial ecology.

Digital PCR (dPCR) is increasingly used for SARS-CoV-2 wastewater surveillance due to its precision, absolute quantification, and reduced sensitivity to inhibition compared to quantitative PCR. Although the Bio-Rad ddPCR and QIAGEN QIAcuity dPCR platforms are widely adopted, their performance has not been directly compared for wastewater applications. We conducted a blinded comparison of these platforms using 95 archived wastewater influent samples from North Carolina collected in 2021-2022, spanning three orders of magnitude in SARS-CoV-2 concentration (1x103 to 5x105 copies L-1). Samples were stratified into low, medium, and high concentration bins and analyzed in triplicate for N1 and N2 gene targets and a bovine coronavirus processing control. Both platforms demonstrated statistically equivalent quantification across all targets, with mean differences [≤]0.12 log copies L-1 (R2 > 0.93). Coefficients of variation were similar (3.96 - 7.61%), with no significant differences across concentration bins except for N2 in the low bin (difference: 0.87 percentage points). Measurement variability correlated strongly with wastewater treatment plant site (R2 = 0.89) rather than platform, indicating that sample matrix characteristics drive precision more than analytical platform. Process limits of detection ranged from 2,160-2,680 copies L-1 for Bio-Rad and 5,650-9,700 copies L-1 for QIAcuity for N1 and N2, respectively. The Bio-Rad platform processed samples 32% faster (305 vs. 435 minutes per 96 wells), while QIAcuity offered 29% lower consumables cost ($4.68 vs. $6.11 per well). These findings support the interchangeable use of both platforms for wastewater surveillance, with platform selection based on laboratory-specific operational needs. ImportanceAs wastewater-based epidemiology transitions from emergency response to sustained public health infrastructure, standardized molecular methods are essential for reliable data integration across surveillance networks. This study provides the first blinded comparison of two digital PCR platforms widely deployed for wastewater pathogen surveillance in the United States. We demonstrate quantitative equivalence between Bio-Rad ddPCR and QIAGEN QIAcuity platforms across three orders of magnitude in viral concentration, establishing that data from both platforms can be interpreted interchangeably for public health decision-making. This platform equivalence is critical as national surveillance systems aggregate data from diverse laboratories and as monitoring expands beyond SARS-CoV-2 to encompass additional respiratory viruses, antimicrobial resistance genes, and emerging pathogens. Our findings provide a methodological foundation for multi-platform surveillance networks and demonstrate that measurement variability is driven primarily by sample matrix characteristics rather than analytical platform choice.

Wastewater surveillance has been widely adopted since the COVID-19 pandemic, but non-sewered or onsite sanitation is a common form of sanitation in cities of low- and middle-income countries. Environmental surveillance in these settings requires expanding analyses beyond wastewater. We collected 81 soil samples adjacent to public waste bins inside the sewered and non-sewered areas of Maputo and a 150-meter-wide buffer zone between the two areas, as well as from subsistence farms near the wastewater treatment plant for comparison. We cultured Escherichia coli (E. coli) using the IDEXX Quanti-Tray/2000 system and determined the prevalence of 29 unique enteric pathogens via RT-qPCR on TaqMan array cards. E. coli concentrations were significantly higher (p<.001) in soils adjacent to public waste bins (mean = 5.05x105 per gram) compared to soils from farms (mean = 8.70x101 per gram). The mean number of unique pathogens was higher in soils from the non-sewered area (mean = 7.9, n=32) and the 150-meter buffer area (mean = 10.5, n=10) compared to the sewered area (mean = 4.6, n=20) and soils from farms (mean=3.8, n=19). Findings demonstrate that the presence of enteric pathogens in soils adjacent to public waste bins were associated with neighborhood sanitation infrastructure and may be a useful matrix for surveillance. In high-burden settings with poor sanitation, direct examination of soils and other environmental matrices are potentially scalable means of environmental pathogen surveillance to consider beyond conventional sampling matrices.

IntroductionWastewater-based epidemiology (WBE) was implemented at the 2024 Republican and Democratic National Conventions (RNC and DNC, respectively)--two prominent large-scale events, each with estimated attendances of >50,000 persons. In preparation for event monitoring, the Wisconsin and Chicago WBE programs (associated with the RNC and DNC public health response, respectively) developed site-specific monitoring strategies and response plans, prioritized additional pathogens for event surveillance, and further optimized laboratory workflows to ensure rapid daily data reporting to public health. The Chicago program expanded the sewer sampling network to include new locations closer to event venues than previously available. Sampling was also conducted before the events, to establish baselines for endemic pathogens, as well as after each event to monitor for residual community transmission. MethodsSurveillance was expanded from the four respiratory pathogens regularly assessed by both WBE programs (SARS-CoV-2, influenza A, influenza B, respiratory syncytial virus) to include 3 gastrointestinal pathogens (norovirus, Salmonella enterica, Shiga toxin-producing E. coli). The Wisconsin program also conducted monitoring for the measles, mumps, rubella, and hepatitis A viruses. Wastewater sampling for the RNC was conducted at the community water reclamation facility level, while at the DNC samples were collected from manholes located downstream of the event venues. For both events, WBE data were summarized and contextualized alongside traditional public health surveillance data in daily situation reports. ResultsBetween the RNC and DNC response, a total of 112 wastewater samples were collected and assayed to provide concentration data on as many as 11 distinct pathogens of interest. Concentration results for the suite of pathogens were available within 12 to 36 hours of sample collection. In each instance when wastewater concentrations exceeded pre-established thresholds for action and flagged as an alert, other sources of contemporaneous public health surveillance information (e.g., clinical data) did not corroborate the WBE findings. ConclusionExisting WBE infrastructure in two U.S. cities was readily adapted for public health surveillance at two high-profile, large-scale events. Assays for additional event-relevant pathogens were quickly incorporated into routine laboratory workflows and data from wastewater samples were generated and reported with rapid turnaround-time. In considering the unique benefits of wastewater data, WBE results were a valuable supplement to other public health surveillance data in monitoring potential public health threats during these two large-scale events.

Structural barriers including limited healthcare access and disability-related health conditions make disabled people differentially susceptible to air pollution-related adverse health outcomes compared to nondisabled people. We used 2020 census-tract level counts of individuals with limitations in activities of daily living (ADLs) to identify a subset of disabled people. We described geographic areas where this population was highly exposed to air pollution in the contiguous U.S., indicating health risk. We assessed census tract-level exposure to PM2.5, O3, NO2 (2016-2020), and wildfire PM2.5 (2016-2023). We mapped high ADL limitation prevalence and high air pollution exposure census tracts. Because environmental injustice means race and poverty strongly predict air pollution exposure, we also assessed exposure among people with ADL limitations by these demographic factors to identify doubly vulnerable subpopulations. High ADL limitation prevalence and PM2.5/NO2 exposure co-occurred in urban areas, Californias Central Valley, Eastern Washington, and parts of the Southeast. Among people with ADL limitations, Asian and Hispanic individuals and those experiencing poverty were more exposed to PM2.5, O3, and NO2. Disability is not fully captured by ADL limitations; future studies should explore other definitions of disability. Future studies should evaluate interventions to reduce air pollution-related morbidity and mortality, especially in regions and subpopulations identified here, where disabled people face high exposure and multiple vulnerabilities.

Accurate quantification of individual exposure to air pollutants remains a major challenge in environmental health, as fixed-site monitoring fails to account for mobility, indoor environments, and physiological variability. We deployed TracMyAir, a smartphone-based digital health platform designed to generate time-resolved, personalized exposure and inhaled dose estimates for PM2.5 and ozone under real-world conditions. In an exploratory study of 18 adults contributing more than 1,500 participant-hours, the platform integrated smartphone geolocation, regulatory (AirNow) and community-based (PurpleAir) air quality data, building infiltration modeling, microenvironment classification, and wearable-derived physical activity metrics to compute eight tiers of hourly exposure estimates, culminating in individualized inhaled dose. Hourly dose estimates derived from smartphone-and smartwatch-based step counts were concordant (Spearman correlation p=0.97-0.98), while heart rate-based estimates yielded greater variability and higher mean values (p=0.82-0.92). Exposure explained 51-73% of variance in inhaled dose of PM2.5 and 68-84% of ozone, suggesting that physiological-based modeling approaches improve hyperlocal estimates of personal pollutant burden. Substantial inter-and intra-individual variability reflect dynamic microenvironmental transitions and activity patterns. Modeled doses based on regulatory and community sensor networks were strongly correlated (R=0.84), with community sensors located closer to participants on average, supporting the feasibility of integrating dense, low-cost monitoring networks. No consistent association was observed between outdoor pollutant levels and neighborhood socioeconomic status in this cohort. These findings demonstrate the feasibility of a scalable, smartphone-centered digital health approach for hyperlocal exposure and inhaled dose modeling. By leveraging ubiquitous consumer devices and existing air quality networks, TracMyAir enables personalized environmental exposure assessment with potential applications in epidemiology, population health, and precision environmental medicine.

The Province of Ontario (Canada) experienced a generational scale outbreak of measles in 2025. We applied wastewater surveillance concurrently with clinical-based surveillance to track measles incidence in southwestern Ontario adjacent to the United States. Measles virus (MeV) signal in wastewater was positively associated with clinical cases but did not provide early alert of changes in measles incidence when resolved by epidemiological week. Assessment of virus partitioning showed MeV RNA was broadly distributed in the liquid phase but is most concentrated in the solids. An assay was adapted for differentiation of vaccine and wildtype MeV and used to detect vaccine genotype measles following an inoculation campaign targeting underserved groups in the region. MeV shedding in wastewater was estimated through repeated sampling of sewer laterals serving a hospital treating confirmed measles infections. This measles outbreak serves as a case study highlighting the application of wastewater surveillance for measles while supporting method development in real-time.

Antibiotic resistance genes (ARGs) circulating among clinically relevant bacteria pose serious challenges to public health. Given the ancient and environmental bacterial origins of ARGs, a better understanding of the carriers of ARGs beyond the clinically most relevant species is urgently needed for more farsighted resistance monitoring and intervention measures. While the risks of emerging ARGs from environmental sources have been recognized, the identification bottlenecks stem from the limitations of shotgun metagenomic sequencing and bioinformatic methods. Here, we used long-read metagenomic sequencing and bacteria-specific methylation profiles to re-establish the links between established (well-described) or latent (absent in databases) ARGs and their bacterial and genetic contexts in wastewater. The base modification data produced by PacBio SMRT sequencing was analyzed by an in-house pipeline utilizing position weight matrices and UMAP visualizations. The approach was validated by a synthetic community with known bacterial composition. Our analysis revealed several previously unreported ARGs and their hosts with varying risk levels defined by their potential as emerging public health threats. For instance, Arcobacter, as one of the prevalent taxa in influent wastewater, was shown to carry a latent beta-lactamase gene with high predicted mobility potential. Of the other emerging beta-lactamases, we provided a real-life example of ongoing pdif module-mediated genetic reshuffling of the blaMCA gene occurring at least within Acinetobacter hosts in our samples. Additionally, we identified Simplicispira, Phycisphaerae, and environmental groups of the Bacteroidales order as the carriers of established, clinically important ARGs. These findings support the intermediate host roles of strictly environmental bacteria for the further dissemination of mobilized ARGs, highlighting the importance of exploring the uncultivated, or non-pathogenic, carriers of ARGs for the early detection of newly arising ARGs and mobility mechanisms.

Antimicrobial resistance (AMR) is a growing global public health threat projected to cause up to 10 million deaths annually by 2050 if no immediate action is taken. While misuse and overuse of antibiotics are the main drivers of increasing AMR, the eco-evolutionary dynamics of AMR in the environment - particularly across the urban-rural continuum - remain poorly understood. Using shotgun sequencing, we investigated urban, farm, and rural water sources in the Berlin-Brandenburg region to explore the distinctness or overlap of their antibiotic resistance gene (ARG) profiles and the potential impact of wastewater treatment plants (WWTP). ARGs were identified using multiple databases and five bioinformatic tools, combining sequence-based alignment and deep learning approaches. This multi-tool approach allowed for the detection of up to 18 AMR classes--more than any single tool alone. The multi-tool screening approach for ARGs, combined with the ABRicate algorithm, was superior to all single ARG tools and databases, detecting more AMR classes, allowing for biocide and metal resistance detection, while less sensitive for detection of aminocoumarin resistance genes. ARG diversity was higher in urban lake sediments, urban waters, and wastewater compared to rural lake sediments and water. Among all environments, urban lake water showed the highest overall ARG abundance, second only to wastewater, and this pattern held across all AMR classes, except for aminoglycoside resistance, which was most prevalent in rural lake sediments. The WWTP was unable to remove the circulating pool of ARGs, despite a decrease in unique ARGs in the outflow.

Robust early warning of emerging viruses requires sampling populations that drive spread coupled with assays capable of detecting new viral variants or species. Untargeted viral metagenomic sequencing can, in principle, detect any virus, including completely novel ones. Composite airplane wastewater enables monitoring long-distance travelers from central collection points; however, the performance of untargeted viral metagenomic sequencing in this sample type remains unknown. In municipal wastewater, abundant sewer-associated microbes and ribosomal RNA depress viral relative abundance, limiting metagenomic sensitivity. We compared untargeted viral metagenomic sequencing of composite airplane wastewater with time-matched municipal wastewater from the Greater Boston area. Human viruses and other human-associated taxa had consistently higher relative abundance in airplane samples than municipal samples, while most sewer-associated taxa had lower relative abundance. An increased relative abundance of human viruses lowers the sequencing depth required to detect emerging pathogens, suggesting that metagenomic sequencing of composite airplane wastewater is a cost-effective method for pathogen-agnostic surveillance. ImportanceLong-distance air travelers spread viral pathogens globally, making them an ideal sentinel population for pandemic surveillance systems. Testing composite airplane wastewater offers a practical, noninvasive approach to monitoring the traveler population. However, current surveillance systems rely on tests targeting specific known pathogens, missing novel threats. Untargeted metagenomic sequencing can detect viruses known or novel, but remains expensive to implement at scale; in municipal wastewater, sewer-derived microbes tend to overwhelm human viruses in sequencing data. We investigated whether a hypothesized reduced sewer microbial load in airplane wastewater would lower the sequencing effort required for viral detection. Understanding the performance of metagenomic sequencing in this context informs the design of cost-effective early-warning systems for emerging pandemics.

We present findings from the first known pilot study of transatlantic airplane wastewater monitoring, conducted over six months at two connected international airports in the United States and the United Kingdom. This study demonstrates the feasibility of implementing bilateral wastewater-based pathogen surveillance at international travel hubs. We outline the operational and analytical methodologies employed, highlight key challenges encountered in transnational coordination, and provide recommendations for the design and implementation of future surveillance programs at points of entry.

Measles outbreaks have surged globally in recent years, but current surveillance systems have limited capacity to monitor measles virus (MeV) transmission and evolution at population scale. Although MeV can be detected in wastewater, the public health potential of wastewater genomic surveillance for MeV remains largely unexplored. Here, we deploy sensitive, low-cost MeV wastewater genomic surveillance combining virus concentration, whole-genome amplicon sequencing, and bioinformatic analysis alongside routine clinical genomic surveillance during the 2024-25 outbreak in South Africa. Integrated phylogenetic analyses of wastewater and clinical MeV genomes revealed previously undetected interprovincial spread and transmission links not captured by standard N450 sequencing. Our findings demonstrate that wastewater-integrated whole-genome surveillance expands the coverage and resolution of routine MeV monitoring and provides a scalable tool to advance measles control and elimination efforts.

Wastewater based epidemiology offers a valuable population level signal for monitoring respiratory virus activity, but its routine use in public health practice requires alerting methods that are transparent, interpretable, and comparable across locations. In this study, we propose a simple early warning framework that transforms wastewater viral RNA measurements into actionable alerts using a standardized statistical process control approach. The method relies on variance stabilization, site specific baseline normalization, and an exponentially weighted moving average to identify sustained increases in viral activity. To support operational relevance, wastewater derived alerts are benchmarked against established laboratory surveillance systems using a harmonized onset definition. The proposed framework emphasizes clarity, auditability and adaptability rather than complex forecasting, enabling straightforward interpretation by public health practitioners. Our results demonstrate that wastewater signals can provide timely situational awareness for respiratory virus circulation and support their use as a complementary tool for public health surveillance and preparedness.

Antimicrobial resistance (AMR) in ESKAPE pathogens represents a major global health threat. Although these organisms are well established as causes of healthcare-associated infections, aquatic environments may function as reservoirs and transmission pathways for resistance. This systematic review aimed to estimate the prevalence of AMR in ESKAPE pathogens isolated from water and wastewater and to compare resistance patterns with those observed in human clinical isolates. The review followed PRISMA guidelines and was registered in PROSPERO (CRD420251020930). PubMed, Embase, and the Cochrane Library were searched to January 14, 2025. Eligible studies were original research reporting antimicrobial susceptibility data for ESKAPE pathogens isolated from both aquatic environmental matrices and clinical samples. Pooled resistance prevalence was estimated using generalized linear mixed models, with heterogeneity assessed using {tau}{superscript 2} and I{superscript 2} statistics and small-study effects evaluated by funnel plots and Eggers test. Of 304 records identified, 18 studies met the inclusion criteria. The pooled overall resistance prevalence was 0.46 (95% CI: 0.36-0.57), with heterogeneity (I{superscript 2} = 98.8%). Resistance was higher in clinical isolates (0.67; 95% CI: 0.55-0.77) than in environmental isolates (0.24; 95% CI: 0.14-0.39), and environmental resistance was greater in effluent-impacted waters than in non-effluent sources. Interpretation is limited by methodological heterogeneity, selective isolation approaches in environmental studies, and imprecision due to small and unevenly distributed samples. Overall, AMR in ESKAPE pathogens remains more prevalent in clinical settings, but aquatic environments, particularly wastewater, represent resistance reservoirs, underscoring the need for standardized methodologies within a One Health framework. Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420251020930, CRD420251020930 HighlightsAntimicrobial resistance was higher in clinical isolates than in aquatic isolates. Resistance patterns showed extreme heterogeneity across studies. Effluent-impacted waters showed higher resistance than non-effluent sources. Higher environmental resistance in some classes reflected methodological artifacts.

Wastewater-based surveillance (WBS) is widely used to monitor respiratory viruses, yet uncertainties remain regarding how viral RNA concentrations in wastewater reflect infection dynamics. Specifically, diurnal variation in shedding and RNA losses during in-sewer transport can impact measured signals. We conducted a field study in a 5-km trunk sewer (travel time of one hour). Wastewater was sampled at the sewer inlet and outlet using autosamplers collecting time-proportional one-hour composite samples over 24 hours. The one-hour composite samples were analyzed for assessing intra-daily fluctuations, and 24-hour composites for signal change. Biofilms from the sewer-pipe walls were collected at three locations. Nucleic acids were extracted, and SARS-CoV-2, Influenza A/B, and Respiratory Syncytial Virus (RSV) RNA were quantified using a multiplex digital PCR assay. All viruses showed pronounced diurnal variation, with consistent morning load peaks. Viral RNA in the bulk liquid decreased during in-sewer transport, with modelled changes ranging from 15% to 72% across pathogens. Biofilms served as minor reservoirs of viral RNA; for SARS-CoV-2, sequencing revealed similarity between biofilm and bulk liquid RNA. Our study provides a full-scale assessment of in-sewer transport effects on viral RNA and highlights the need to account for complex in-sewer dynamics when interpreting WBS data.

Wastewater-based epidemiology (WBE) is an effective surveillance approach for monitoring viruses of public health relevance at the community level, complementing clinical surveillance systems. Molecular methods such as PCR/qPCR are widely used for targeted detection, while next-generation sequencing (NGS) with targeted enrichment panels has emerged as a complementary strategy for broader viral detection and genomic characterization. This study comparatively evaluated conventional PCR/qPCR and a targeted enrichment whole-genome sequencing Viral Surveillance Panel (VSP, Illumina) for virus detection in wastewater. Fifty-six wastewater samples collected between 2017 and 2023 from a wastewater treatment plant in Cordoba, Argentina, were concentrated by polyethylene glycol precipitation and pooled by season and year, reaching a total of 14 pools. Each pool was analyzed in parallel by PCR/qPCR for eight human viruses of public health importance and by the VSP, targeting 66 viral species, sequenced on a NovaSeq 6000 platform, and analyzed with the DRAGEN pipeline. Detection frequencies for each virus using PCR/qPCR and VSP were: RoV A 100%/14.3%; NoV 100%/14.3%; AiV 50%/42.9%; SARS-CoV-2 14.3%/0%; HAV 42.9%/0%; HEV 14.3%/0%; JCPyV 35.7%/85.7%; BKPyV 28.6%/71.4%, respectively. In addition, VSP detected the genomes of Astrovirus (71.4%), Salivirus (21.4%), Coxsackie A (14.3%), Rotavirus C (14.3%), and Merkel Cell virus (7.1%), and enable the recovery of 16 near complete genomes (coverage > 92.5%) of AiV, JCPyV, BKPyV, Salivirus and Astrovirus. PCR/qPCR and targeted enrichment NGS provide complementary information wastewater viral surveillance. Their combined application improves virus detection and genomic characterization, reinforcing the value of integrated approaches in environmental virology and public health monitoring.

Acute heat exposure, which is increasing with climate change, likely increases preterm birth risk. However, few studies consider susceptible exposure windows for extreme heat events, particularly among historically unexposed populations. The 2021 Pacific Northwest Heat Dome produced the highest temperatures ever recorded in usually temperate Oregon and Washington State, offering an ideal study setting. We used 2016-2022 vital statistics records to estimate the gestation month-specific impact of the Heat Dome on preterm birth. Using an interrupted time series design with a synthetic control, we compared the observed odds of preterm birth in the exposed (in utero November 2020-July 2021) Oregon and Washington conception cohorts to counterfactual odds had the Heat Dome not happened. Analysis included 716,096 exposed births across 67 monthly conception cohorts. We identified increased odds of preterm birth in cohorts exposed during months 2-3 (11% increase, 95% CI: [1%, 22%]) and 6-7 (14% increase, 95% CI: [5%, 24%]) of pregnancy. These findings partially agree with literature reporting elevated preterm birth risk after heat exposure in all trimesters. As extreme heat events are now expected once to twice per decade rather than once every thousand years, they pose risks to perinatal health.

Emerging infectious diseases often circulate undetected until they cause clinical illness, creating serious risks for public health and biosecurity. The U.S. blood supply, with millions of routinely collected and quality-controlled donations, offers an untapped national resource for proactive pathogen surveillance. We propose integrating metagenomic sequencing (MGS) into existing blood and plasma collection workflows to detect novel or unexpected viruses in deidentified residual samples. This approach complements environmental sampling from wastewater and air by enabling detection of blood-borne and vector-borne pathogens that cause asymptomatic viremia, threats that may be poorly captured by other systems. Our modeling analysis suggests that this system is highly cost-effective: an estimated annual investment of approximately $5.8 million could detect a novel HIV-like pathogen before it infects 0.01% of the population. Building on existing blood donor testing infrastructure, privacy frameworks, and an analogous national serosurveillance network established during COVID-19, MGS blood monitoring represents a deployment-ready, cost-effective addition to national biosecurity infrastructure, transforming the blood supply from a safety measure into an early warning system for emerging viral threats.

BackgroundEnvironment-wide association studies (ExWAS) offer a systematic approach to identifying chemical biomarker-health outcome associations, yet few have applied rigorous multi-stage validation. MethodsWe screened 92 chemical biomarkers against 48 health outcomes in NHANES 2017-2018 (2,796 tests across four screening rounds; not all chemicals were crossed with all outcomes). Associations passing an initial FDR screen were subjected to cross-cycle validation in NHANES 2015-2016--the primary inferential safeguard given the adaptive screening design--followed by dose-response analysis and multiple sensitivity specifications. Survey-weighted regression models adjusted for age, sex, race/ethnicity, poverty-income ratio, BMI, and smoking. ResultsOf 26 associations passing FDR correction, 21 were testable in cross-cycle validation; of these, 15 (71%) replicated with concordant direction and p < 0.05 in a temporally independent NHANES 2015-2016 sample. Of these 15, 14 remained robust after analyte-specific sensitivity checks; urinary creatinine adjustment identified one association (iodine-BMI) as a dilution artifact. Two novel findings emerged: dimethylarsonic acid with uric acid ({beta} = 0.20 mg/dL per log-unit DMA, 95% CI: 0.15-0.26) and urinary perchlorate with BUN ({beta} = 1.21 mg/dL per log-unit perchlorate, 95% CI: 0.97-1.45); a third high-novelty association (methylmercury-waist circumference) is likely explained by fish consumption patterns. ConclusionsMulti-stage ExWAS with cross-cycle validation identified 14 robust chemical-health associations. Two novel findings--DMA-uric acid and perchlorate-BUN--survived all sensitivity checks and warrant prospective investigation.

Candida auris is a multidrug-resistant fungal pathogen that presents substantial challenges for healthcare facilities due to its high mortality rates among vulnerable populations. Six C. auris clades have been identified based on their susceptibility to antifungal treatment and environmental stressors. Identifying the circulating C. auris clade(s) is critical for understanding transmission and selecting a disease control strategy. To inform targeted implementation of community wastewater monitoring for C. auris, samples were collected over 34 weeks from 8 nursing homes and 6 downstream wastewater treatment plants (WWTPs). Detection rates and concentrations of C. auris DNA were significantly higher in samples from nursing homes compared to those from WWTPs. Amplicon sequencing methods were developed and applied to characterize the circulating C. auris clade in a nursing home wastewater sample. This study demonstrates the utility of wastewater monitoring as a resource-efficient approach for detecting and subtyping C. auris in vulnerable communities.