GeoHealth

BackgroundWest Nile virus (WNV) infection has caused over 30,000 human cases of the severe, neuroinvasive form of the disease (West Nile virus Neuroinvasive Disease; WNND) and nearly 3,000 deaths in the U.S. since its introduction in 1999. Despite spatiotemporal variation in the impact of WNV and its known links to various climate factors, no effective nationwide WNV or WNND forecast exists. ObjectivesWe aim to produce a skillful, nationwide WNND forecast built upon regionally varying relationships between climate factors and WNND. MethodsWe examined the impact of climate conditions on annual WNND caseload for 11 ecologically meaningful regions in the U.S. The most salient climate factors were incorporated into a regionally determined nationwide WNND forecast model. We retrospectively generated forecasts from 2005-2022 using observed climate conditions and compared forecast skill against various benchmarks, including a simple, historical case-driven model. Forecast skill was assessed by weighted interval scoring. ResultsRegional, climate-informed WNND retrospective forecasts outperformed a benchmark model only informed by historical WNND case data across all regions, as well as in a nationally aggregated score (univariate: 18.1% [3.7-27.0%], bivariate: 23.9% [9.0-32.8%]). Additionally, the regional forecasts outperformed an ensemble model generated from the 2022 CDC WNV Forecasting Challenge and a parallel, county-level, regional climate-informed forecast outperformed forecasts from the same Challenge. Drought and temperature were the climate factors most consistently linked to WNND and incorporated into our forecast model. DiscussionWe show a retrospectively generated WNND forecast for the continental U.S. that considerably improved upon simple forecasts based on historical case distributions. This forecast aggregated county-level data to broader regions to boost statistical signal and capture the regionally varying influences of climate conditions on annual WNND caseload. The advances here represent a potential path toward actionable broad-scale WNV forecasts.

Understanding how climate modulates infectious disease dynamics is critical for anticipating epidemic patterns. This study examines the association between climatological variables--specifically temperature and relative humidity--and the incidence of the SARS-CoV-2 Omicron variant (B.1.1.529) during its global wave (2021-2022). Using global epidemiological and climate data, we applied Scale-Dependent Correlation (SDC) analysis to detect transient, scale-specific associations across regions and periods. We identified consistent negative correlations between incidence and both temperature and humidity, especially in mid-latitudes during colder months. These findings were compared with predictions from stochastic population-based compartmental models incorporating climate-dependent transmission parameters. Among the tested formulations, the temperature-based model achieved the best fit to observed case trajectories. Our results highlight a robust climatological influence on Omicron transmission dynamics and underscore the importance of integrating climate indicators into epidemic modeling and preparedness strategies.

Spatial microsimulation models have recently emerged as a new method to quantify health impacts associated with climate change for policy decision-support. These individual-based methods, previously used in tax and health policy planning, have been adapted by combining climate data with exposure-response associations to estimate the distributional health impacts attributable to climate hazards using synthetic populations. To evaluate their methodological characteristics, we conducted a systematic review of the literature. We searched five electronic databases, Google Scholar and the International Journal of Microsimulation, and screened 762 articles to reach a final study set of seven articles. Most models simulated populations based in high income countries (n=5) and applied dynamic methods to forecast future health outcomes (n=5). Multiple diverse climate-health pathways of impact were investigated, ranging from heatwave mortality to air pollution-induced cardiovascular outcomes, to climate-sensitive infectious disease occurrence. Baseline and projected spatial climate data was mapped to individuals in city, state, or regional-level synthetic populations to allocate personal hazard exposure. Most models included socio-economic and demographic attributes (n=6) to integrate vulnerabilities for burden assessments in marginalised groups such as children, women, and the elderly. Climate policies mainly focused on mitigation and simulated future emissions scenarios (n=5), or policy mixes (n=1); one study tested an incremental adaptation intervention. Methods to enhance decision-support among alternative policy options such as economic evaluation (n=2) or stakeholder engagement (n=3) were under-represented. All models acknowledged uncertainty of parameters, and most reported uncertainty analyses (n=5). High data needs may limit accessibility of these methods in some contexts, however options to build on existing models and improve data and computing power access could overcome these challenges. This systematic review documents this evolving, state of the art application of microsimulation and finds a promising and versatile quantitative tool for health impact assessments and climate policy decision-support.

Young children (from birth to 5 years old) are uniquely vulnerable to environmental hazards due to their higher exposure relative to body weight, rapid physiological and neurological development, and strong reliance on caregivers for protection and care. Such risks are often amplified in marginalized communities with socioeconomic disadvantage and limited access to resources. However, widely used indices, such as the Social Vulnerability Index (SVI), the Climate Vulnerability Index (CVI) and the Child Opportunity Index (COI), were not specifically developed for young children and may not capture the combined environmental and socioeconomic risks faced by this age group. To address this critical gap, we developed a county-level Early Childhood Environmental Health Vulnerability Index (EC-EHVI) for the contiguous U.S. using multidimensional indicators within an Exposure-Sensitivity-Adaptive Capacity framework and informed by Bronfenbrenners bioecological model. We identified the underlying drivers and the spatial patterns of the EC-EHVI. Our results showed that the EC-EHVI exhibited the strongest association with county-level young child mortality and explained a larger proportion of spatial heterogeneity compared with the SVI, CVI, and COI. Elevated vulnerability clustered in the Great Plains and Southeastern U.S., where over half of high-risk counties were exposure-driven, and 411 high-high hotspots were identified. The EC-EHVI offers a valuable spatial decision-support tool for designing targeted, place-based interventions and advancing environmental health equity for young children. Plain Language SummaryYoung children (birth to age five) are uniquely vulnerable to environmental hazards. Because their bodies are developing and they consume more air, food, and water relative to their weight, environmental exposures can have severe, lifelong impacts. These risks are often magnified in under-resourced communities. Yet, most existing vulnerability tools were not built with young children in mind, potentially obscuring the combined environmental and social threats they face. To address this gap, we developed a new county-level index to pinpoint where young children are most at risk across the contiguous United States. Our tool integrates data on environmental exposure, community sensitivity, and the resources available to help families cope. When tested, our new index was more strongly linked to young child mortality than several widely used existing measures. We identified major high-risk clusters, particularly in the Great Plains and the Southeastern U.S. This tool can help policymakers and public health officials better target resources and interventions to protect young children and promote environmental health equity. Key PointsO_LIWe developed a county-level Early Childhood Environmental Health Vulnerability Index across the contiguous U.S. C_LIO_LIElevated vulnerability clustered in the Southeast, Great Plains, and Appalachia, with additional hotspots in Michigan and Maine. C_LIO_LIMore than half of high-vulnerability counties were exposure-driven, emphasizing the key role of environmental hazards in child health. C_LI

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.

The COVID-19 pandemic had led to 500000 confirmed death by June 30, 2020. We combined the number of monthly confirmed new cases and deaths with latitude, temperature, humidity, rainfall, and sunshine ultraviolet (UV) to explore the climate impacts on COVID-19 fatality in 88 countries. There was a significant decrease in overall case-fatality rate in May and June (from 8.17% to 4.99% and 3.22%). The fatality in temperate marine regions was the highest (11.13%). The fatality was 5.71% in high latitudes ([&ge;]30{degrees}) but only 3.73% in low latitudes (<30{degrees}). The fatality was 6.76% in cold regions (<20{degrees}C) but only 3.90% in hot regions ([&ge;]20{degrees}C). The fatality was 5.87% in rainy regions ([&ge;]40mm) but only 3.33% in rainless regions (<40mm). The fatality was 6.57% in cloudy regions (<50) but only 3.86% in sunny regions ([&ge;]50). Traveling to hot sunny regions without pollution is a strategy for risk reduction.

Early detection of outbreaks and emerging pathogens is critical for public health and global biosecurity. Airports, as major international travel hubs with dense, enclosed populations, are high-risk settings for disease transmission and potential pathogen introduction. The U.S. Centers for Disease Control and Prevention, in collaboration with Ginkgo Biosecurity and the University of Wisconsin-Madison, implemented air monitoring for pathogen surveillance in congregate areas at four U.S. international airports. From October 2023 to August 2024, SARS-CoV-2 was detected by PCR in 98.3% of air samples and influenza A in 17.2%. These results correlated with positivity trends from other sample modalities, including aviation wastewater, traveler nasal swabs, and national clinical surveillance data. Targeted amplicon sequencing of SARS-CoV-2 from air samples correlated with contemporaneous lineages in wastewater collected and sequenced from the same airports. Metagenomic enrichment sequencing detected 30 viral species and recovered high-quality genomes for SARS-CoV-2, influenza, bocavirus, and seasonal coronaviruses. Together, these findings demonstrate that air sampling is a complementary surveillance modality to aviation wastewater for early pathogen detection at ports of entry.

Climate change has many adverse human health effects, including increased anxiety. However, eco-anxiety may also motivate climate action. An online survey was developed and distributed to examine factors associated with eco-anxiety. Logistic regression analysis showed that significant predictors of eco-anxiety include greater media exposure to climate change information, more frequent discussions about climate change with friends and family, the perception that climate change will soon impact one personally, being younger, and being female. Additional analyses suggested that ecoanxiety was associated with a range of both positive and negative emotional impacts including motivation, interest, sadness, and tension. Eco-anxiety was also associated with greater likelihood to engage in environmental behaviors such as recycling. Volunteering for environmental causes and accessing straightforward information with less scientific jargon were found to have particular potential for anxiety reduction among the eco-anxious. The research suggests practical strategies to reduce eco-anxiety while retaining engagement in mitigating climate change.

BackgroundCoccidioidomycosis is an emerging fungal disease caused by inhaling Coccidioides spp. spores. As spores reside in the soil, activities that disturb soil may aerosolize and transport the pathogen, yet the types of activities facilitating transmission remain poorly understood. MethodsWe conducted a case-crossover study to estimate the association between exposure to new oil and gas wells (ie, those in preproduction) and risk of coccidioidomycosis among nearby residents. We obtained information on coccidioidomycosis cases reported between 2007 and 2022 in Kern County, California--a county among the top in both oil and gas production and coccidioidomycosis incidence. We compared exposure to preproduction wells within 5 km of each patient residence during "hazard" and "control" periods using conditional logistic regression. FindingsDuring the study period, 73% of Kern County residents lived within 5 km of at least one preproduction well, and 13% lived within 5 km of [&ge;]23 preproduction wells within a single 90-day period. We estimated that the odds of coccidioidomycosis were 11.0% higher (95% CI: 4.3-18.1%) in the 90 days following exposure to at least one preproduction well within 5 km of the patient residence and that the odds of infection increased by 0.7% (95% CI: 0.4-1.0%) for each additional preproduction well within this distance. InterpretationWe identified a previously unrecognized association between oil and gas development and the transmission of an emerging infectious disease. Given the prevalence of oil and gas development in the study region, its impact on coccidioidomycosis incidence there may be large. Research in ContextO_ST_ABSEvidence before the studyC_ST_ABSCoccidioidomycosis is an emerging fungal disease caused by the inhalation of airborne Coccidioides spores. As these spores reside in the soil, activities that disturb soil--such as construction, farming, earthquakes, and dust storms--have previously been associated with elevated transmission risk. However, the full range of activities that facilitate pathogen transmission remains poorly characterized. Here, we investigated whether oil and gas development contributes to coccidioidomycosis risk as this process involves several soil-disturbing steps (eg, site clearing, leveling, movement of heavy equipment), and occurs at high intensity in an endemic region for the disease. To assess existing evidence, we searched PubMed from database inception to May 27, 2025, for articles published in English using search terms "oil well" OR "gas well" OR "oil and gas construction" OR "oil and gas development" AND "infectious disease" OR "transmission" OR "risk", and their common textual variants. We identified 31 relevant studies investigating associations between oil and gas development and adverse health outcomes, including preterm birth, low birth weight, cancer diagnoses, upper respiratory symptoms, and all-cause mortality. Only one prior study investigated infectious disease outcomes, finding that high levels of exposure to oil and gas production were associated with moderately elevated COVID-19 severity. We found no prior studies investigating the impact of oil and gas development on the risk of coccidioidomycosis or any other environmental pathogens. Added value of the studyThis study identifies a previously unrecognized adverse health outcome of oil and gas development. Using a case-crossover study design focused on Kern County, California--one of the top seven oil-producing counties in the U.S.--we found the development of new oil and gas wells was associated with elevated coccidioidomycosis risk for individuals within five kilometers. Associations were strongest when the well was developed during fall or summer months, when dry soils may be most readily aerosolized. Further, we found that approximately 73% of Kern County residents lived near at least one well developed over the study period (2007-2022), and 13% lived near [&ge;] 23 wells developed within a single 90-day period. Given this high level of exposure, oil and gas development may be an important driver of transmission in this highly endemic region. Implications of all the available evidenceOur study adds to a growing body of evidence of harmful health impacts of oil and gas development. It also identifies a novel pathway of exposure risk for an emerging fungal disease. As there are currently no vaccines available for coccidioidomycosis and few effective antifungal drugs, identifying and mitigating environmental exposures to fungal spores is critical for protecting public health.

Seasonal temperature variation may impact the trajectories of COVID-19 in different global regions. Cumulative data reported by the World Health Organization, for dates up to March 27, 20201, show association between COVID-19 incidence and regions at or above 30{degrees} latitude. Historic climate data also show significant reduction of case rates with mean maximum temperature above approximately 22.5 degrees Celsius. Variance at the local level, however, could not be well explained by geography and temperature. These preliminary findings support continued countermeasures and study of SARS-CoV-2/COVID-19 transmission rates by temperature and humidity.

HighlightsO_LIIn March 2023, Cyclone Yaku was followed by a large dengue epidemic in northwest Peru C_LIO_LIExtreme precipitation during Cyclone Yaku caused 60% of dengue cases C_LIO_LIMore cyclone-attributable cases occurred in warm, urban, flood-susceptible districts C_LIO_LIGlobal warming has increased the risk of warm, very wet March weather in the region C_LI Science for SocietyAnthropogenic climate change is increasing the risk of extreme weather that can lead to infectious disease epidemics, but few studies have directly measured this health consequence of climate change. Extreme precipitation can drive outbreaks of mosquito-borne diseases by displacing people, disrupting public health activities, and creating aquatic breeding habitat. Here, we quantify the effects of extreme precipitation during Cyclone Yaku in northwestern Peru in March 2023. The cyclone was immediately followed by a dengue outbreak where cases exceeded historic averages by tenfold. We estimate that 60% of cases (or 22,014 cases) reported over three months in the affected districts were attributable to extreme precipitation during Cyclone Yaku. Compared with the preindustrial era, extremely wet March conditions were 31% more likely to occur (and 189% more likely to co-occur with warm temperatures suitable for dengue transmission) in recent decades in northwestern Peru. Assessing the linkages between climate change, extreme weather, and outbreaks of dengue and other infectious diseases is crucial for understanding the current impacts of climate change and for preparing for future health risks. eTOC SummaryThis study examines relationships between historical climate forcing, extreme weather, and health, focusing on Cyclone Yaku and Perus 2023 dengue epidemic. Historical climate forcing has increased the likelihood of extreme precipitation coinciding with warm temperatures suitable for transmission in March in northwest Peru. In turn, extreme precipitation during Cyclone Yaku caused a majority of dengue cases in the epidemic, especially across warmer districts. Extreme weather, made more likely by climate change, is already having an impact on human health. Climate change is increasing the likelihood of extreme weather that can drive outbreaks of climate-sensitive diseases. For example, dengue burden has recently increased rapidly with unusually warm and wet conditions. However, linkages between historical climate change, extreme weather, and mosquito-borne disease have not been traced quantitatively. Here, we analyze the contribution of extreme precipitation to dengue in northwestern Peru during Cyclone Yaku in March 2023. Using generalized synthetic control methods, we estimate 60% of cases were attributable to extreme precipitation and more cyclone-attributable dengue cases occurred in warmer, more flood susceptible, and more urban districts. Historical climate forcing has increased the likelihood of concurrent extreme precipitation and warm temperatures suitable for dengue transmission in northwestern Peru in March by 189%. This case study is one of the first to estimate cases of mosquito-borne illness caused by extreme weather conditions and shows those conditions were made more likely by climate change.

Climate change detection and attribution (D&A) methods have become vital for quantifying the influence of anthropogenic forcing on the Earth's systems, including human health. Health impact attribution (HIA) studies seek to disentangle climate-driven health effects from natural variability yet are often constrained by the availability of accessible counterfactual climate scenarios. This tutorial paper presents a flexible, reproducible framework for developing counterfactual climates without reliance on computationally intensive global circulation models. We provide practical, R-based methodologies for constructing both trend-based (temperature and non-temperature) and event-based counterfactual, using a variety of techniques including model residual detrending, data-driven decomposition (e.g., Singular Spectrum Analysis and Empirical Mode Decomposition) and stochastic weather generators. The tutorial also explores the incorporation of greenhouse gas concentrations as forcing variables, rather than global mean temperature anomalies. By operationalising these methods through worked examples and an open code repository, this paper aims to build capacity within the HIA community, enhance methodological transparency, and foster interdisciplinary collaboration between climate and health researchers.

This study presents the discovery and characterization of novel microscopic structures found on the surface of a rock sample collected from a stream in Pereira, Risaralda, Colombia. The structures, termed microcondrulos, exhibit spherical morphologies atypical of known terrestrial entities. Qualitative and semi-quantitative chemical analyses were performed using scanning electron microscopy with energy-dispersive X-ray spectrometry (SEM-EDS). Results revealed a complex elemental composition, with up to forty-eight different elements detected, including C, O, N, Si, Ti, V, Ni, La, and Ce, among others. The microcondrulos were differentiated from known terrestrial contaminants such as pollen, bacteria, and various protist groups. Comparative analysis with previously reported stratospheric samples and micrometeorites suggests a possible non-terrestrial origin. These findings contribute to the ongoing discussion on the diversity of life and the potential for alternative biogenesis. A taxonomic proposal for these new entities is presented for the first time.

Bat-borne viruses are a threat to global health and have in recent history had major impacts to human morbidity and mortality. Examples include diseases such as rabies, Ebola, SARS-Cov-1, and SARS-Cov-2 (COVID-19). Climate change could exacerbate the emergence of bat-borne pathogens by affecting the distribution and abundance of bats in tropical ecosystems. Here we report an assessment of historical climate and vampire bat occurrence data for the last century, which revealed a relationship between climatic variation and risk of disease spillover triggered by changes in bat distributions. This report represents one of the first examples of empirical evidence of global change effects on continental patterns of bat-borne pathogen transmission risk. We therefore recommend that more research is necessary on the impacts of climate change on bat-borne pathogen spillover risk, and that climate change impacts on bat-borne disease should be considered in global security initiatives. HighlightsO_LIBat-borne viruses are a threat to global health and include diseases such as rabies, Ebola, SARS-Cov-1, and SARS-Cov-2 (COVID-19). C_LIO_LIClimate change could exacerbate the emergence of bat-borne pathogens by affecting the distribution and abundance of bats. C_LIO_LIHere we report an assessment of historical climate and vampire-bat occurrence data for the last century, which reveals a relationship between climatic variation and risk of disease spillover triggered by changes in bat distributions. C_LI

As new cases of COVID-19 are being confirmed pressure is mounting to increase understanding of the factors underlying the spread the disease. Using data on local transmissions until the 23rd of March 2020, we develop an ensemble of 200 ecological niche models to project monthly variation in climate suitability for spread of SARS-CoV-2 throughout a typical climatological year. Although cases of COVID-19 are reported all over the world, most outbreaks display a pattern of clustering in relatively cool and dry areas. The predecessor SARS-CoV-1 was linked to similar climate conditions. Should the spread of SARS CoV-2 continue to follow current trends, asynchronous seasonal global outbreaks could be expected. According to the models, temperate warm and cold climates are more favorable to spread of the virus, whereas arid and tropical climates are less favorable. However, model uncertainties are still high across much of sub-Saharan Africa, Latin America and South East Asia. While models of epidemic spread utilize human demography and mobility as predictors, climate can also help constrain the virus. This is because the environment can mediate human-to-human transmission of SARS-CoV-2, and unsuitable climates can cause the virus to destabilize quickly, hence reducing its capacity to become epidemic.

BackgroundLyme disease is the most common vector-borne illness in the United States. Incidence is related to specific environmental conditions such as temperature, metrics of land cover, and species diversity. ObjectiveTo determine whether greenness, as measured by the Normalized Difference Vegetation Index (NDVI), and other selected indices of land cover were associated with the incidence of Lyme disease in the northeastern USA, 2000-2018. Materials and MethodsWe conducted an ecological analysis of incidence rates in counties of 15 "high" incidence states and the District of Columbia for 2000-2018. Annual counts of Lyme disease by county were obtained from the US Centers for Disease Control and values of NDVI were acquired from the Moderate Resolution Imaging Spectroradiometer instrument aboard Terra and Aqua Satellites. County-specific values of population density, area of land and water were obtained from the US Census. Using quasi-Poisson regression, multivariable associations were estimated between the incidence of Lyme disease NDVI, land cover variables, human population density, and calendar year. ResultsWe found that incidence increased by 7.1% per year (95% confidence interval: 6.8-8.2%). Land cover variables showed complex non-linear associations with incidence: average county-specific NDVI showed a u-shaped" association, the standard deviation of NDVI showed a monotonic upward relationship, population density showed a decreasing trend, areas of land and water showed "n"-shaped relationships. We found an interaction between average and standard deviation of NDVI, with the highest average NDVI category, increased standard deviation of NDVI showed the greatest increase in rates. DiscussionThese associations cannot be interpreted as causal but indicate that certain patterns of land cover may have the potential to increase exposure to infected ticks and thereby may contribute indirectly to increased rates. Public health interventions could make use of these results in informing people where risks may be high.

IntroductionTemperature extremes, including elevated heat and cold, are important environmental determinants of health whose frequency and duration are increasing due to climate change. Ecological and time-series studies have established links with adverse outcomes but often lack individual-level detail. Electronic health records (EHR) provide an alternative source, yet their use in climate-health research remains inconsistent. MethodsWe conducted a rapid review of peer-reviewed studies using EHR data to examine associations between temperature extremes and health outcomes across healthcare settings. The aim was to assess how health impacts of temperature extremes have been captured and coded within EHR-based research, and to identify methodological and coding-related gaps. Searches of seven databases identified eligible studies, and data were extracted on exposure definitions, outcome coding, methods, findings, and limitations. ResultsOf 1,616 records identified, 526 duplicates were removed, leaving 1,090 for screening; 58 studies met inclusion criteria. Extreme heat was most frequently studied, with fewer analyses of cold. Common outcomes included morbidity, cardiovascular admissions, asthma, and pregnancy-related conditions. Mental health outcomes were rarely examined, subgroup analyses were mostly age-based, and studies focused on high-income countries. Exposure metrics and coding practices varied widely, with limited reporting of diagnostic codes and individual-level mediators. ConclusionHarmonized exposure definitions, broader outcome coverage, and integration of socio-demographic and individual-level factors are needed to strengthen EHR-based climate-health research and guide targeted interventions.

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.

Escalating wildfire frequency and severity, exacerbated by shifting climate patterns, pose significant ecological and economic challenges. Prescribed burns, a common forest management tool, aim to mitigate wildfire risks and protect biodiversity. Nevertheless, understanding the impact of prescribed burns on soil and microbial communities in temperate mixed forests, considering temporal dynamics and slash fuel types, remains crucial. Our study, conducted at the University of Tennessee Forest Resources AgResearch and Education Center in Oak Ridge, TN, employed controlled burns across various treatments, and the findings indicate that low-intensity prescribed burns have none or minimal short-term effects on soil parameters but may alter soil nutrient concentrations, as evidenced by significant changes in porewater acetate, formate, and nitrate concentrations. These burns also induce shifts in microbial community structure and diversity, with Proteobacteria and Acidobacteria increasing significantly post-fire, possibly aiding soil recovery. In contrast, Verrucomicrobia showed a notable decrease over time, and other specific microbial taxa correlated with soil pH, porewater nitrate, ammonium, and phosphate concentrations. Our research contributes to understanding the intricate relationships between prescribed fire, soil dynamics, and microbial responses in temperate mixed forests in the Southern Appalachian Region, which is valuable for informed land management practices in the face of evolving environmental challenges.