GeoHealth

The impact of weather on the health of Americans and the American health system is substantial. Using available health and economic data, we developed a data-driven scenario that describes a compounded heat emergency in an archetypal community in the United States. We then characterize the potential human and economic costs of such a heat emergency to demonstrate the widespread impact on health outcomes, health systems, and society.

Dengue fever risk and burden has increased globally in the past decade, with record-breaking outbreaks driving high case numbers, outbreaks increasing in existing transmission suitable regions, and occurring in new locations. A combination of global change processes, including climate change, have provided the environmental backdrop for introductions and resurgences of mosquito-transmitted dengue virus. Understanding shifts in exposure risk is integral to public health preparedness. This study provides global mapping of the thermal suitability of dengue transmission for CMIP6 climate scenarios, across a range of general circulation models (GCMs), and we created spatially explicit demographic projections of transmission risk using year-matched RCP-SSP frameworks for demographic and emissions scenarios. Globally, poleward shifts in projected distributions of suitability for transmission for both Ae. aegypti and Ae. albopictus suitability are shown in both the near term (2030s) and longer term (2050). Under a 'middle of the road' climate scenario (CMIP6 SSP2-4.5), regions in Africa and Asia are the major areas driving increases in year-round (12 months) population at risk (PAR) through 2050, with an anticipated net gain in 932 million people at risk for Ae. aegypti transmission and 24 million for Ae. albopictus, which includes multiple regions losing areas of year-round suitability as temperatures exceed the higher thermal boundary for transmission. In contrast, the estimated net increase in PAR for one or more months of transmission suitability at a global scale by 2050 is 3.29 billion people for Ae. aegypti transmission and 3.30 billion for Ae. albopictus transmission. This snapshot of a 'middle-of-the-road' combination of climate and demographic driven increases in potential dengue transmission exposure emphasizes the importance of both expanding suitability in new areas, and growing populations in areas approaching and becoming exposed year-round. Globalization, urbanization, and shipping will continue to provide the potential for introductions into newly suitable areas as season lengths increase, sparking outbreaks in unexposed populations. This is compounded and becomes ever more probable as the number of people and places at year-round risk also increases. This project provides all global gridded outputs for onward mapping and reuse, to add to the toolkit to anticipate and prepare for prevention and response to dengue in a changing world.

Residential greenness has been associated with reduced heat-related illness, yet the specific role of greenspace morphology at the neighborhood scale remains insufficiently understood. This study quantified the relationship between heat-related illness and multiple dimensions of greenspace morphology using an eight year (2016-2023) unbalanced panel dataset comprising 19,021 block group year observations across 2,427 census block groups in Arizona, USA. One meter high resolution National Agricultural Imagery Program aerial imagery was classified to calculate greenspace percentage, number of greenspaces, average size, shape complexity, connectedness, and distantness, at the block group level. We applied conditional spatial autoregressive models with a negative binomial distribution to estimate associations between each morphology metric and yearly heat-related illness counts, adjusting for sociodemographic and geographic covariates. We found higher greenspace percentage, aggregation, shape complexity, connectedness, and density were consistently associated with lower heat-related illness risk. A one standard deviation increases in shape complexity corresponded to a 12.4% decrease in expected heat-related illness counts (IRR=0.876, 95% CI: 0.834-0.921). Similarly, increases in greenspace percentage (14.6% decrease; IRR=0.855, 95% CI: 0.827-0.885), number of greenspace patches (3.7% decrease; IRR=0.963, 95% CI: 0.937-0.990), average size (4.5% decrease; IRR=0.955, 95% CI: 0.923-0.989), and connectedness (5.5% decrease; IRR=0.945, 95% CI: 0.918-0.972) were all protective. In contrast, larger inter greenspace distances were associated with increased heat-related illness risk (6.1% increase; IRR=1.061, 95% CI: 1.033-1.091). Our findings highlight the critical importance of multiple dimensions of greenspace morphology in mitigating heat-related health risks. These results suggest that heat reduction planning with greening initiatives should consider not only the amount of greenspace but also its spatial configuration to maximize cooling and result in health benefits.

Short-term environmental exposures have been linked to cognitive and attention-related outcomes, but the robustness of these associations remains uncertain. We linked daily weather and air-pollution exposures to repeated measures of subjective cognitive difficulties and attention-related outcomes among participants in the All of Us Research Program from 2018 to 2024. Associations were evaluated using complementary longitudinal and causal-inference approaches, including fixed-effects, lagged-exposure, and event-study analyses. Machine-learning methods were used to characterize heterogeneity and latent psychosocial structure, and findings were independently evaluated using 2024 Behavioral Risk Factor Surveillance System data. Several environmental exposure measures were associated with cognitive outcomes in pooled analyses; however, most associations attenuated substantially after accounting for within-location temporal variation. In contrast, mental-health burden, loneliness, and impaired social functioning remained consistently associated with subjective cognitive difficulty across analytical approaches. Similar patterns were observed in the validation dataset. These findings suggest that some observed environmental associations may reflect broader geographic and contextual differences rather than short-term environmental effects. Overall, psychosocial factors demonstrated more consistent associations with subjective cognitive difficulties than short-term environmental exposures across multiple analytical frameworks and independent datasets.

Fine particulate air pollution (PM2.5) in the United States has fallen by roughly half since 2000, yet linked health outcomes such as diabetes and childhood ADHD have not improved in parallel. One reconciling possibility is that pollution exposure in early life produces health effects that emerge only years or decades later, after pollution itself has declined. Using two decades of U.S. county-level data, we relate annual PM2.5 estimates to birth outcomes, diabetes prevalence, and small-area estimates of childhood attention-deficit/hyperactivity disorder (ADHD) across short and long time scales. Within counties, changes in low birth weight rates are associated with changes in PM2.5 during the same year and the year prior to birth. At longer time scales, cross-county comparisons show that PM2.5 exposure is associated with higher prevalence of adult diabetes and ADHD after approximately a decade. Together, these patterns suggest that population-level health risks from air pollution may persist over decades, even as pollution itself declines.

Background: Surgical care centralization in the U.S. delays access and increases carbon emissions. Global targets suggest patients live within 2-hours of a surgical facility. This study quantifies the environmental impact of travel for cataract surgery in rural Michigan and models the potential emissions reductions from decentralizing surgical and follow-up services. Methods: A retrospective, cross-sectional study analyzed electronic medical records from a rural Michigan ophthalmology practice (March-November 2023). We calculated travel distances using population-weighted centroids and estimated emissions using U.S. Department of Energy vehicle data. A k-means clustering model optimized additional facility placement, and a gradient analysis identified optimal numbers for decentralization points, for emissions reductions. Results: The 920 patients traveled a median of 55.45 km (IQR: 43.33-88.20 km) for surgery and 55.07 km (IQR: 43.54-87.82 km) for follow-up visits, generating Total Surgical Access Emissions (TSAE) of 57,168 kgCO2; (median of 59.20 kgCO2; IQR: 32.31-81.87) under the centralized model. The k-means decentralization model and gradient analysis identified 7 hospitals and 9 clinics, respectively, as the optimal expansion points, reducing emissions by 34.07% (19,475 kgCO2 saved) and 39.52% (22,590 kgCO2; saved). The Surgical Access Carbon Impact (SACI) model demonstrated that achieving two-hour access to clinic services reduced excess emissions by 54.7%. Sensitivity analyses using fuel-efficient vehicles (Toyota Prius and Tesla Model 3) or reducing follow-up visit frequency reduced emissions by 54.03% (30,888 kgCO2) and 25.83% (14,768 kgCO2), respectively. Conclusion: Decentralizing surgical services in rural U.S. settings could cut travel-related emissions by up to 40%, significantly reducing healthcare-related carbon footprints while improving timely access to care. The SACI metric provides a novel framework for integrating environmental sustainability into U.S. health policy and service planning

Background Wildfire activity and smoke exposure are increasing worldwide because of climate and land-use change. Although fine particulate matter (PM2.5) may impair pulmonary immune defences against tuberculosis (TB), population-level evidence remains limited. We estimated the effect of wildfire-related PM2.5 exposure on TB notification rates in Brazil. Methods We conducted a nationwide panel study linking municipality-level monthly TB notifications from Brazil's SINAN system with wildfire-related PM2.5 estimates from GEOS-Chem simulations across 5,545 municipalities (2003-2023). We estimated the impact of high-exposure days (PM2.5 > 25 g/m3) on monthly TB notifications using Poisson regression with fixed effects for municipalities, state-by-year, and state-by-month, controlling for time-invariant differences, secular trends, and seasonality. Distributed lag effects were estimated over 1-24 months before notification. Models accounted for meteorological conditions, GeneXpert diagnostic coverage, and spatial correlation using Conley standard errors. We computed attributable fractions among exposed municipality-months (AFE). Sensitivity analyses evaluated alternative PM2.5 thresholds (15 and 35 g/m3), co-pollutants, and agricultural expansion. Findings From Jan 1, 2003 to Dec 1, 2023, 1,758,982 TB cases were reported. Of these, 353,319 (20.1%) had at least one high-exposure day (PM2.5 > 25 g/m3) 1-24 months before notification. An additional 14 high-exposure days over the 24-month lag period was associated with an average monthly increase of 2.9% [95% CI: 0.9-4.9%] in TB notification rates. Effects peaked at 13 months (IQR: 11-14) prior to notification. Results showed a dose-response relationship across PM2.5 thresholds and were robust to controlling for NO2, O3, and agricultural expansion. Overall, wildfire-related PM2.5 exposure accounted for 2.1% [0.7-3.5%] of TB notifications in exposed municipality-months, corresponding to 7,802 [2,612-12,544] attributable cases. The AFE reached 10.7% [7.1-14.0%] in Pantanal and 7.3% [6.1-8.5%] in Amazonia, areas most impacted by wildfires. Interpretation Wildfire-related PM2.5 exposure may represent an increasingly important and modifiable risk factor for TB. As wildfire activity increases across many regions of the world, these findings highlight the need for integrating air quality into climate adaptation and TB control strategies.

Tropical dryland ecosystems are highly biodiverse and fragmented and are experiencing significant anthropogenic and climatic changes. With increasing extremes in temperature and precipitation, coupled with significant alteration, these ecosystems are at greater risk of increased exposure and vulnerability to climatic change; however, little work has quantified the climatic shifts occurring within these ecosystems globally. Here, we aim to fill this gap by using the ERA-5 reanalysis and CHIRPS precipitation data to quantify changes in essential climatic variables in tropical drylands since 2000. Overall, we find that regional pressures differ, with tropical dry forests, savannas, and shrublands becoming hotter and drier in the Neotropics and parts of the Afrotropics and Australasia. By contrast, the tropical dry forests in the Indomalayan, Oceania, and Nearctic are experiencing hotter and wetter conditions. Globally, though, these ecosystems are experiencing more change than the global average, suggesting they may be approaching tipping points in their resilience, ultimately shrinking the area where they can survive.

Exposure to higher levels of ambient air pollution during pregnancy has been linked to multiple adverse pregnancy outcomes. However, studies on acute exposures and reduced gestation length have reported inconsistent findings. This project aims to examine the acute association between ambient air pollution and preterm (28-36 gestational weeks) or early-term (37-38 gestational weeks) births. Daily concentrations of 12 air pollutants, based on bias-corrected numerical model outputs, were linked to vital records of singleton live preterm and early-term births from 2005-2017 in California, Florida, Georgia, Kansas, Nevada, New Jersey, North Carolina (2005-2015), and Oregon. Under a time-stratified case-crossover design, odds ratios (OR) were estimated via conditional logistic regression with adjustment for risks among ongoing pregnancies, meteorology, time trends and federal holidays. We estimated cumulative associations up to a 6-day lag using distributed lag models. Risk estimates per interquartile range (IQR) increase in exposure were pooled across states using inverse-variance weighting. Our study included 1,085,162 preterm and 3,901,185 early-term births. We observed positive associations between 0-2 day cumulate exposure to several air pollutants and early-term births, including NO2 (OR: 1.0023, 95% CI:1.0010, 1.0037 per 7.1 g/m3 increase), PM2.5 (OR=1.0022, 95% CI: 1.006, 1.0038 per 4.6 g/m3 increase), PM2.5 organic carbon (OR= 1.0026, 95% CI: 1.0013, 1.0039 per 1.7 g/m3 increase) and PM2.5 elemental carbon (OR=1.0025, 95% CI: 1.0014, 1.0035 per 0.26 g/m3 increase). Associations with preterm birth were mostly null. In conclusion, we found positive associations between short-term air pollution exposure, including PM and major PM2.5 components, and risks of early-term birth.

Abstract Background: Despite considerable reductions in under-five mortality during the Millennium Development Goal era, progress towards Sustainable Development Goal (SDG) 3.2 remains uneven across Africa. Identifying countries at greatest risk of missing the target is essential for prioritizing interventions and resource allocation. Methods: A Bayesian spatial forecasting ecological study was conducted using 2024 country-level data from 49 African countries obtained from UNICEF. Spatial dependence was assessed using Global Moran's I and Local Indicators of Spatial Association. Bayesian structured additive regression models with Gaussian, Gamma, and Exponential likelihoods were fitted using Integrated Nested Laplace Approximation (INLA) and compared using the Deviance Information Criterion (DIC), Watanabe-Akaike Information Criterion (WAIC), and conditional predictive ordinates. Posterior exceedance probabilities were estimated, an SDG Failure Index (SFI) and a Priority Intervention Index (PII) were developed, and Bayesian posterior predictive simulations were performed to estimate country-specific probabilities of attaining SDG 3.2 by 2030. Results: Significant spatial clustering of under-five mortality was observed with (Moran's I = 0.355, p < 0.001), and hotspots in Benin, Cameroon, and Nigeria. The Gamma model provided the best fit (DIC = 114.92; WAIC = 111.71). Diarrhoea was the only significant predictor (posterior mean=0.030; 95% credible interval: 0.004-0.056). Twenty-three countries (46.9%) were classified as high risk, whereas only five (10.2%) had achieved SDG 3.2. West Africa recorded the highest mean mortality (7.05%) and North Africa the lowest (1.64%). Bayesian projections indicated that only five countries were likely to achieve SDG 3.2 by 2030, while 41 (83.7%) were unlikely to do so. Conclusion: Considerable geographical inequalities in under-five mortality persist across Africa, and most countries remain off-track for achieving SDG 3.2 by 2030. The integration of exceedance probability mapping, the SDG Failure Index, the Priority Intervention Index, and Bayesian probability forecasting provides a practical framework for monitoring progress and prioritizing countries requiring accelerated action towards achieving SDG 3.2.

Background Coccidioidomycosis is an emerging fungal disease across the arid Americas and a frequent cause of community-acquired pneumonia. Understanding where Coccidioides populations originate, how they move across space, and whether they are expanding is important for interpreting changing patterns of Valley fever and anticipating future infection risk. Methods We prospectively collected and whole-genome sequenced 186 Coccidioides-positive clinical isolates submitted to a national diagnostic laboratory, and included 126 previously sequenced genomes. We applied genomic clustering, time-calibrated phylogenetic reconstruction, ancestral area reconstruction, mating-type assignment, and demographic inference to identify major populations, infer dispersal patterns, assess evidence for recombination and clonality, and reconstruct historical population dynamics. Findings We analyzed 312 genomes (139 C. immitis; 173 C. posadasii) and identified three major genetic populations within each species. C. immitis included two California-centered populations and one Pacific Northwest population, whereas C. posadasii included two Arizona-centered populations and one Texas-centered population. The most recent common ancestor was estimated at approximately 127,000 years for C. immitis and 234,000 years for C. posadasii. Most populations were not fully monophyletic, consistent with retained ancestral variation and/or ongoing gene flow. Inferred dispersal was largely asymmetric, with most movement originating from California in C. immitis and from Arizona and Texas in C. posadasii. Most populations contained both mating types, but one C. immitis population and a Brazilian subgroup of C. posadasii were clonal. All populations showed recent demographic expansion. Interpretation The evolutionary history of Coccidioides is characterized by strong geographic structure, ongoing gene flow, and recent demographic expansion. These processes are likely to influence future patterns of Valley fever endemicity and supports the use of genomic surveillance to detect shifts in disease risk as environmental conditions change.

Background Brucellosis is a severe zoonotic disease with pronounced seasonality and regional heterogeneity in high-incidence areas of China. Reliable forecasting tools are needed to inform prevention strategies, but the optimal modeling approach across different regions remains unclear. Principal Findings We collected monthly brucellosis incidence and 17 environmental variables from 2014 to 2024 across five high-incidence provinces: Inner Mongolia, Xinjiang, Shanxi, Heilongjiang, and Hebei. A three-step procedure--cross-correlation analysis, multicollinearity diagnostics, and stepwise regression--was used to select exogenous predictors. We then compared four time-series models: seasonal autoregressive integrated moving average (SARIMA), SARIMA with exogenous variables (SARIMAX), long short-term memory (LSTM), and LSTM with exogenous variables (LSTMX). All five provinces showed a unimodal seasonal pattern with peaks between April and July, though environmental drivers and optimal lag periods varied substantially by region, ranging from 1 to 6 months. In forecasting performance, LSTM achieved the highest accuracy in Shanxi (R2=0.925), Hebei (R2=0.876), and Xinjiang (R2=0.829), outperforming SARIMA and SARIMAX. LSTMX performed best in Inner Mongolia (R2=0.759) and Heilongjiang (R2=0.772) but showed weaker performance than LSTM in Shanxi and Hebei. Overall, adding exogenous variables did not consistently improve predictions across provinces. Conclusions Our findings demonstrate that LSTM-based models offer clear advantages for brucellosis forecasting in most high-incidence provinces, but the value of incorporating environmental predictors is region-dependent. These results support the development of tailored early warning systems and precision prevention strategies for brucellosis in high-risk areas of China.

Background: Climate change is an escalating global health threat, with sub-Saharan Africa disproportionately affected due to entrenched spatial inequalities, high exposure to environmental hazards, and limited adaptive capacity. Increasingly frequent extreme weather events (EWEs), including floods and cyclones, are reshaping the material and social conditions of place, with implications for mental health and wellbeing. However, evidence remains limited, particularly multi-country qualitative research that examines how mental health impacts are produced through lived experiences of place in contexts of recurring environmental disruption and structural vulnerability. This study explored the mental health and wellbeing impacts of EWEs among individuals with lived experience of such events in Mozambique, Burkina Faso, South Africa, and Kenya, using participatory methods that centred community narratives and place-based accounts of everyday life. Methods: This qualitative study employed digital storytelling as a participatory visual method to examine how EWEs are experienced and narrated across diverse socio-spatial contexts. A total of 37 participants (8 to 10 per country) were recruited from rural, peri-urban, and informal urban settlements with recent exposure to flooding or cyclone events. Participants produced digital stories during facilitated five-day workshops. These narratives were analysed using inductive and deductive thematic analysis informed by Braun and Clarke's framework, with attention to the spatial and relational production of distress and coping. Results: Across Mozambique, Burkina Faso, South Africa, and Kenya, findings show that the mental health impacts of EWEs are deeply embedded in place-based conditions and are cyclical, cumulative, and relational rather than confined to discrete disaster events. Participants described how repeated environmental disruptions reconfigured everyday life in place, generating ongoing uncertainty, anticipatory anxiety during rainfall periods, and acute fear during floods and cyclones. Loss of housing, livelihoods, infrastructure, and social anchors of place contributed to enduring psychological distress, which was frequently reactivated by subsequent environmental cues such as heavy rain, wind, and deteriorating physical environments. Persistent anxiety, hypervigilance, sleep disturbance, and emotional distress were reported across all sites. While social and community networks constituted critical infrastructures of care within place, these were often simultaneously overwhelmed as entire communities experienced shared disruption. Limited and delayed institutional responses further compounded spatial and social precarity. Conclusions: This study provides a comparative participatory account of how EWEs shape mental health through their embeddedness in place across diverse sub-Saharan African contexts. The findings demonstrate that psychological distress is produced through the interaction of repeated environmental exposure, structural inequality, and disrupted place-based infrastructures of daily life, rather than emerging solely as a post-disaster outcome. These results underscore the need for climate-responsive mental health and psychosocial support that is integrated into place-based disaster risk governance, alongside strengthened social protection and community infrastructure that can sustain wellbeing in contexts of recurring environmental instability.

Climate and land use change are reshaping the dynamics of vector-borne diseases. West Nile virus (WNV), the most widespread zoonotic arbovirus in the United States, illustrates the need to integrate climate, land cover, and social vulnerability across heterogenous landscapes when assessing spatial risk. We present a nationwide, county-level assessment of WNV risk, using complementary statistical and mechanistic models to (1) identify socio-ecological correlates of current WNV incidence, and (2) project vector species-specific, temperature-dependent transmission suitability under mid- and late-century climate change scenarios. We find that land cover gradients, temperature-driven transmission, and both occupational and residential exposure are associated with WNV incidence, particularly in mixed urban-agricultural landscapes. Future temperature and land cover projections suggest spatially variable shifts in environmental risk, driven by divergent physiological responses among Culex species vectors. Our results highlight temperature and land cover as consistent, mechanistically grounded correlates of WNV risk at the national scale, while underscoring the need for refined, species-specific analyses at local levels. These insights can inform more targeted surveillance, vector control, and climate adaptation strategies. We also identify key knowledge gaps, particularly around host and vector ecology, that must be addressed to improve public health response in the face of ongoing environmental change.

Advances in air-based surveillance of pathogen genetic material are hindered by reliance on centralized, time-consuming molecular techniques. Point-of-care (POC) diagnostic platforms, like the Cepheid(R) GeneXpert(R), offer rapid, simplified testing in clinical settings but have not been evaluated for use with air samples. Here, we paired the ThermoFisherTM AerosolSenseTM air sampler with the Xpert(R) Xpress SARS-CoV-2/Flu/RSV Plus test to evaluate near-real-time air surveillance. To assess analytical sensitivity, we spiked collection substrates with inactivated viruses and performed overnight sampling using the air sampler. As few as 10 copies of influenza A/B (IAV/IBV) and RSV applied to the substrate were detectable by GeneXpert, while SARS-CoV-2 required at least 100 copies for detection. Longitudinal air surveillance was conducted across congregate settings in Columbia, Missouri, and Madison, Wisconsin, in 2024-2025, collecting 281 air samples. SARS-CoV-2 was detected most frequently, followed by IAV. To assess concordance, 191 samples with paired GeneXpert and RT-qPCR results were analyzed across multiple Ct value cutoffs. Agreement between GeneXpert and RT-qPCR for SARS-CoV-2 was fair to moderate (K = 0.306-0.443). Optimal GeneXpert Ct cutoffs for the best balance between sensitivity and specificity, determined using analyses such as Youden's index, were site-specific: 45 for Wisconsin (67% sensitivity, 83% specificity) and 41 for Missouri (76% sensitivity, 62% specificity), reflecting differences in laboratory protocols. For IAV, agreement was moderate (K = 0.56) with GeneXpert Ct cutoff of 40, achieving 85% sensitivity and 81% specificity. Further studies across diverse settings and viral targets are needed to establish GeneXpert's role in routine air surveillance.

Abstract Objectives: To estimate surveillance-adjusted county-level residual syphilis risk, quantify posterior support for elevated risk, and identify the geographic distribution of stably high-risk areas across the contiguous United States and the District of Columbia. Methods: County-year primary and secondary syphilis counts from 3,109 counties during 2010-2022 were analyzed using a Bayesian negative-binomial spatial model with county-level covariates capturing social vulnerability and healthcare and surveillance related structure. Residual spatial risk, posterior exceedance probabilities, and stably high-risk counties were estimated. External validation examined whether county-level residual syphilis risk was associated with HIV and gonorrhea burden. Results: A total of 850 stably high-risk counties were identified. These counties were concentrated in the southeastern United States and along the Gulf Coast, with additional clusters in the north-central region and along the Atlantic and Pacific coasts. The social vulnerability index showed the strongest positive association with reported syphilis rates, followed by primary care physician density. External validation and sensitivity analyses showed that higher county-level residual syphilis risk estimates were positively associated with higher HIV diagnosis rates and gonorrhea rates, indicating that these estimates were not merely model-derived numerical outputs but were meaningfully related to the county-level distribution of sexually transmitted infection risk. These findings indicate that surveillance-adjusted residual spatial risk estimates and posterior exceedance probabilities may provide useful county-level evidence for syphilis control prioritization and resource allocation.

Wastewater surveillance (WS) has been widely adopted as a cost-effective and population-representative infectious disease monitoring tool and is increasingly being applied to bacterial and antimicrobial resistance gene (ARG) targets. However, some of these targets may persist in pipe biofilms and detach into wastewater, complicating accurate WS interpretation. To investigate biofilm contributions to wastewater pathogen and ARG signals, paired sink-drain biofilm, branch-drain-plumbing biofilm (sewer biofilm), and wastewater were collected from five hospital sites over a four-month period and analyzed using 16S rRNA gene amplicon sequencing and probe-capture metagenomics. Overall, sewer biofilm bacterial communities were as diverse as wastewater. Across sites, a mean of 9% (0.9 to 23.3%) of wastewater bacterial communities could be attributed to sewer biofilm communities. Many clinically relevant pathogens were consistently detected both in sewer biofilm and wastewater, including environmentally persistent and/or biofilm-associated taxa (e.g., Pseudomonas aeruginosa, Klebsiella pneumoniae). While many ARGs overlapped between wastewater and biofilms (e.g., tetA, sul1, blaCTX-M, vanA), others were significantly enriched in sewer biofilms (e.g., qacL, van-operon and OXA genes). Together, these findings confirm that wastewater pathogen and resistome profiles integrate inputs from both human shedding and pipe-resident communities and therefore need to be considered when selecting WS targets and interpreting signal.

Wolbachia-mediated incompatible-insect technique (IIT) via wAlbB, wMel or wPip/wAlbA/wAlbB strains are promising approaches for suppressing wildtype Aedes mosquitoes and therefore Aedes-borne diseases. Yet, the effectiveness of this technique under climate change remains uncertain. Here, we evaluate the long-term robustness of male Wolbachia-infected mosquito releases to suppress wildtype Aedes aegypti and Ae. albopictus populations across future climate scenarios across diverse geographical regions. We compiled large publicly available datasets on Aedes abundance across Singapore, China, the European Union and the United States, historical and projected climatic conditions in these regions and conducted experiments to test the thermal stability of cytoplasmic incompatibility in Wolbachia-infected male Aedes aegypti and albopictus. A climatically-driven entomological model was developed and calibrated using a Bayesian approach to model observed Aedes population dynamics and infer area-specific climate-driven variation in mosquito life-history traits. We back-inferred historical mosquito abundance and projected mosquito abundance in future climate change scenarios incorporating experimental and locally inferred entomological parameters and then simulated the counterfactual implementation of IIT in these regions. We find that Aedes populations are projected to increase in most regions across all climate change scenarios from 2050-2100 even under high heat conditions in the absence of interventions. While we found that IIT can suppress wild-type populations effectively across all future scenarios and in high heat conditions, effectiveness was found to depend heavily on mosquito emigration rates, overflooding ratios, release intervals and release strategies Extensive robustness checks confirmed that the model reproduced historical temporal trends, captured the influence of individual parameters on outcome and was sensitive to changes in values of inferred parameters and implement policy. These findings demonstrate that IIT may be a robust vector control tool under future climate conditions.

ContextThe global diversity hotspot in Southwest Australia has >480 orchids facing increasing threats from climate extremes, fire and habitat decline. AimsTo develop effective and consistent tools for measuring climate impacts on productivity in a diverse urban orchid community. MethodsAnnual variations in flower and seed production for 17 orchids were determined using thousands of records over a decade with extreme climate variability. Key resultsRainfall deficits and temperatures in autumn, winter and spring increased substantially over 125 years. Seasonal climate anomalies reduced flowering and seed production for orchids, but this varied between species and seasons. These effects were summarised by climate response (CRI) and sensitivity (CSI) indexes. Early or late flowering species were most vulnerable to seasonal drought, and visually deceptive pollination preferred warm dry conditions. CRIs were strongly correlated with orchid pollination syndromes and flowering times. Effects on mycorrhizal fungi and pollinators were also observed. Extrapolating climate trends to 2100 predicted further impacts on orchid productivity (-5-40%). ConclusionsOrchid climate responses were diverse and deeply integrated with pollination, phenology, fire sensitivity and other key traits. ImplicationsResearch in an urban climate observatory produced a climate analysis framework that is likely relevant to many orchids and other biota.

Background: Men with prostate cancer (PCa) may be especially vulnerable to per- and polyfluoroalkyl substances (PFAS) exposure due to their endocrine-disrupting and cardiometabolic impacts and cardiotoxicity and immune suppression of treatments. Objective: A pilot study was launched to measure serum and tap water PFAS concentrations in PCa survivors. Methods: Men with PCa were recruited from Rutgers Cancer Institute between February 2025 and March 2026, with ongoing enrollment and follow-up. Eligible men were aged [&ge;]40 years and either on active surveillance or within 3-12 months of initial definitive treatment. Participants provided blood and residential tap water samples, which were analyzed using mass spectrometry (serum) and modified EPA method 537 (water). Geometric means were used to summarize PFAS concentrations by race and assess serum-tap water correlations. Results: Of 235 eligible patients, 124 (60%) enrolled. Median age was 64 years; 63% were non-Hispanic White, 43% had a Gleason score [&le;]6. Roughly half of participants provided serum and/or tap water samples. In serum, six PFAS analytes had >80% detection; of these analytes, median concentrations ranged from 0.13 ng/mL (IQR: 0.07-0.20) for PFHpS to 2.55 ng/mL (IQR:1.54-3.82) for nPFOS. Among 74 tap water samples, 9 PFAS analytes had >60% detection; of these, median concentrations of PFNA (0.56 ng/L; IQR: 0.33-0.75), PFOA (3.75 ng/L; IQR: 1.21-5.27), and PFOS (2.29 ng/L; IQR: 0.46-2.89), were below New Jersey Maximum Contaminant Levels. Non-White participants had significantly higher levels of multiple PFAS analytes in both serum and tap water. Serum-tap water correlations were modest (r=0.22-0.41). Significance: The pilot study has demonstrated both the feasibility and importance of studying PFAS exposure pathways as well as potential impacts of PFAS exposure in diverse populations. Keywords: Prostatic Neoplasms, Per- and Polyfluoroalkyl Substances (PFAS), Biomonitoring, Environmental Exposure, Cohort Studies, Pilot study Impact Statement: This study provides some of the first estimates of PFAS exposure among prostate cancer patients in serum and tap water, showing moderate correlations between tap water and serum concentrations of specific PFAS analytes. These findings can support larger studies to identify environmental exposure sources and evaluate the role of PFAS in prostate cancer progression and outcomes.