GeoHealth

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

Climate change is likely to exacerbate a range of determinants which drive tuberculosis, the worlds leading infectious disease killer. However, tuberculosis is often neglected in wider climate health discussions. Commissioned by the World Health Organization, we developed an analytical framework outlining potential causal relationships between climate change and tuberculosis. We drew on existing knowledge of tuberculosis determinants, identified which are likely to be sensitive to the effects of climate change, and conceptualised the mechanistic pathways through which this might occur. We collated evidence for these pathways through literature reviews. Our reviews found no studies directly linking climate change and tuberculosis, warranting research to build evidence for action. The available evidence supports the existence of plausible links between climate change and tuberculosis, and highlights the need to include tuberculosis in climate risk adaptation and mitigation programmes, and climate-resilient funding and response mechanisms. Further evidence is urgently needed to quantify the effects of climate change on tuberculosis.

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.

BackgroundThe COVID-19 pandemic has challenged even the most robust public health systems world-wide, leaving state and local health departments, hospitals, and physicians with little guidance on planning and resource allocation. Efforts at predicting the virus spread have largely failed to capture the nuances presented by national and local geographic, environmental, and sociological variables. ObjectiveUsing county-level data from the United States, we sought to measure the extent to which these demographic, geographic, and environmental variables correlate with the spread of COVID-19. MethodsUsing demographic data from the US Census Bureaus American Community Survey, weather station data from the National Oceanic and Atmospheric Administration (NOAA), and COVID-19 case data from the Center for Systems Science and Engineering at Johns Hopkins University and the New York State Department of Health, we employed Bayesian hierarchical modeling with zero-inflated Negative Binomial regression to calculate correlations between these variables, COVID-19 case count, and rate of viral spread. Key predictors were identified and measured during two periods of two weeks each: March and June of 2020. The resultant model was then employed to predict case counts and spread rate for early July 2020. ResultsWhile demographic and environmental factors explain viral spread well, our findings challenge earlier conclusions about how these factors related to viral progress. Using these factors alone, we were able to predict spread to within 1% in all but 8 counties (99.9%), and within 0.1% in all but 51 counties (98.4%). The model was subsequently able to predict early July viral spread to within 0.5% in 98% of counties. Contrary to earlier findings, temperature had variable effect; as Spring temperatures warmed, cases decreased, but Summer heat increased cases, likely reflecting movement of populations from indoors to outdoors and back in. States varied little in their case rate relative to the model, and much of the variation could be linked to known "superspreader" events. ConclusionWhile environmental and demographic variables can help predict COVID-19 spread rates, some relationships are variable in ways earlier research failed to identify. Role of Funding SourceThere was no funding supporting this work.

Urban heat islands and climate change create increasingly hot environments that pose a threat to the health of the public in urban areas throughout the planet. In Maricopa County, Arizona, --- the hottest metropolitan area in the United States---we have previously shown that the effects of heat on mortality are greater in the social and built environments of low-income and communities of color (predominantly Hispanic/Latinx and Black neighborhoods). In this analysis of morbidity data from Maricopa County, we examined the relationship between heat-related hospitalization and summertime daily maximum air temperatures in groups defined at the census block group level as being at high, medium, or low vulnerability based on a Heat Vulnerability Index that was derived from socio-economic and built-environment data. For all three categories of census block group heat vulnerability, we identified 26{degrees}C as the daily maximum air temperature threshold beyond which heat-related hospitalization risk increased rapidly with each 1 {degrees}C increase in temperature. Compared to this baseline temperature, the relative risk of hospitalization was greatest in the high vulnerability census block groups and least in the low vulnerability census block groups with intermediate increases in the medium vulnerability census block groups. Specifically, with 26{degrees}C as the referent, the relative risks of heat-related hospitalization increased from 0.97 at 27{degrees}C to 15.71 at 46{degrees}C in the low vulnerability group, from 1.03 at 27{degrees}C to 53.97 at 46{degrees}C in the medium vulnerability group, and from 1.09 at 27{degrees}C to 162.46 at 46{degrees}C in the high vulnerability group. Our research helps identify areas with high heat population sensitivity and exposure that can be targeted for adaptation with policies and investments, which include, for example, improving public health safety nets and outcomes, access to affordable energy-efficient housing and health care, energy justice, and modifications to cool the urban built environment. Our hospitalization risk estimates can be incorporated into quantitative risk assessments of heat-related morbidity in Maricopa County.

Since the onset of the COVID-19 pandemic, researchers mainly examined how socio-economic, demographic, and environmental factors are related to disparities in SARS-CoV-2 infection rates. However, we dont know the extent to which racial disparities in environmental exposure are related to racial disparities in SARS-CoV-2 infection rates. To address this critical issue, we gathered black vs. white infection records from 1416 counties in the contiguous United States. For these counties, we used 30m-spatial resolution land cover data and racial mappings to quantify the racial disparity between black and white peoples two types of environmental exposure, including exposures to various types of landscape settings and urban development intensities. We found that racial disparities in SARS-CoV-2 infection rates and racial disparities in exposure to various types of landscapes and urban development intensities were significant and showed similar patterns. Specifically, less racial disparity in exposure to forests outside park, pasture/hay, and urban areas with low and medium development intensities were significantly associated with lower racial disparities in SARS-CoV-2 infection rates. Distance was also critical. The positive association between racial disparities in environmental exposures and racial disparity in SARS-CoV-2 infection rates was strongest within a comfortable walking distance (approximately 400m). HighlightsO_LIRacial dot map and landcover map were used for population-weighted analysis. C_LIO_LIRacial disparity in environmental exposures and SARS-CoV-2 infection were linked. C_LIO_LIForests outside park are the most beneficial landscape settings. C_LIO_LIUrban areas with low development intensity are the most beneficial urban areas. C_LIO_LILandscape and urban exposures within the 400m buffer distances are most beneficial. C_LI

Climate change-related weather and extreme events are increasing in intensity and frequency, affecting infectious disease transmission globally. Dengue, a climate-sensitive vector-borne disease, to which over half the worlds population is at risk of infection, has expanded its geographical range over recent decades. The 2023/24 season marked the largest ever dengue outbreak year in the Americas, coinciding with the hottest year on record in the Americas. Here, we use statistical models to investigate the Brazil 2023/24 dengue season and attribute how anthropogenic climate change impacted it. We analyze >20 years of dengue data across >5000 municipalities and find that observed temperature anomalies in municipalities of southern Brazil pushed those locations into optimal thermal conditions for dengue transmission. In contrast, in northern Brazil, 2023/24 temperatures became too high for effective transmission, resulting in lower dengue incidence compared to a counterfactual scenario without anthropogenic climate change. We test the generalizability of our model to high altitude areas in Mexico, where dengue has been expanding. Our work empirically demonstrates how a climate-change-related temperature anomaly led to the range expansion and growth of dengue across variable ecological and socio-economic settings, with implications for preparedness, adaptation, mitigation, and resilience planning.

The threats posed by COVID-19 have catalyzed a search by researchers across multiple disciplines for policy-relevant findings about critical risk factors. We contribute to this effort by providing causal estimates of the link between increased chronic ambient pollutant concentrations and the intensity of COVID-19 disease, as measured by deaths and hospitalizations in New York City from March through August, 2020. Given concerns about unobservable characteristics that contribute to both ambient air pollutant concentrations and the impacts of COVID-19 disease, we instrument for pollutant concentrations using the time spent downwind of nearby highways and estimate key causal relationships using two-stage least squares models. The causal links between increases in concentrations of our traffic-related air pollutants (PM2.5, NO2, and NO) and COVID-19 deaths are much larger than the correlations presented in recent observational studies. We find that a 0.16 g/m3 increase in average ambient PM2.5 concentration leads to an approximate 30% increase in COVID-19 deaths. This is the change in concentration associated with being downwind of a nearby highway. We see that this effect is mostly driven by residents with at least 75 years of age. In addition to emphasizing the importance of searching for causal relationships, our analysis highlights the value of increasing the density of pollution-monitoring networks and suggests potential benefits of further tightening of Clean Air Act amendments, as our estimated effects occur at concentrations well below thresholds set by the National Ambient Air Quality Standards.

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 [≥]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 [≥] 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.

Human health in urban environment has emerged as a primary focus of sustainable development during the time of global pandemic caused by a severe acute respiratory syndrome due to the SARS-CoV-2 coronavirus, COVID-19. It has reshaped the world with the way our communities interact, people work, commute, and spend their leisure time. While different mitigation solutions for controlling COVID-19 virus transmission have already been established, global models that would explain and predict the impact of urban environments on the case fatality ratio CFR of COVID-19 (defined as the number of deaths divided by the number of cases over a time window) are missing. Here, with readily available data from public sources, we study the CFR of the coronavirus for 118 locations (city zip-codes, city boroughs, and cities) worldwide to identify the links between the CFR and outdoor, indoor and personal urban factors. We show that a probabilistic model, optimized on the sample of 20 districts from 4 major US cities, provides an accurate predictive tool for the CFR of COVID-19 regardless of the geographical location. When adjusted for the population, our model can be used to evaluate risk and severity of the disease at multi-geospatial scales worldwide ranging from zip-codes and neighborhoods to cities and countries for different waves of the pandemic. Our results suggest that although disease screening and vaccination policies to containment and lockdowns remain critical in controlling the spread of airborne diseases, urban factors such as population density, humidity, or order of buildings, should all be taken into consideration when identifying resources and planning targeted responses to mitigate the impact and severity of the viruses transmitted through air. We advocate the study of urban factors as a path towards facilitating timely deployment of targeted countermeasures and confinement strategies where sharing of personal information and availability of tests may be restricted or limited.

BackgroundSolar geoengineering, whereby sunlight is reflected back into space at the outer atmosphere to reduce incoming sunlight, is increasingly considered a viable option to mitigate global warming, yet the health consequences of reducing incoming sunlight remain poorly quantified. ObjectivesThis study examines the effects of sunlight exposure on the rate of suicide across the United States over nearly three decades and projects the impact of geoengineering-induced reductions in sunlight on suicides by 2100. MethodsThe analysis relates sunlight exposure, as measured by solar insolation, to the suicide rate at the county-by-month level in the United States between 1979 and 2004 (N = 444,861), after adjusting for temperature, precipitation, county-by-month effects, and state-by-year effects. We project the excess suicides due to the negative radiative forcing required to keep the temperature rise below 1.5 {degrees}C by 2100. ResultsWe find that suicide rates increase by 6.99% (95% CI: 3.86, 10.13) as sunlight decreases by one standard deviation, which is almost equivalent to the difference in sunlight between the lowest (Vermont) and highest (Arizona) state-level averages. The effects are similar across an extensive set of county characteristics and over time, suggesting limited adaptation to sunlight exposure in suicidal behavior. We also find that insufficient sunlight exposure increases the searches containing depressive language on Google Trends. These estimates suggest that solar geoengineering could result in 1.26-3.18 thousand additional suicides by 2100 under the business-as-usual scenario, which could more than offset the suicides averted by temperature fall. DiscussionOur findings highlight the substantial benefits of sunlight exposure on the incidence of suicide and mental well-being, thus calling for climate policy to better balance the potential benefits and harms of solar geoengineering.

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.

Aedes aegypti spread pathogens affecting humans, including the dengue, Zika and yellow fever viruses. Anthropogenic climate change is altering the spatial distribution of Ae. aegypti and therefore the locations at risk of vector-borne disease. In addition to climate change, natural climate variability, resulting from internal atmospheric processes and interactions between climate system components (e.g. atmosphere-land, atmosphere-ocean) determines climate outcomes. However, the combined effects of climate change and natural climate variability on future Ae. aegypti spread have not been assessed fully. We developed an ecological model in which Ae. aegypti population dynamics depend on climate variables (temperature and rainfall). We used 100 projections from the Community Earth System Model, a comprehensive climate model that simulates natural climate variability as well as anthropogenic climate change, in combination with our ecological model to generate a range of equally plausible scenarios describing the global distribution of suitable conditions for Ae. aegypti up to 2100. Like other studies, we project the poleward expansion of Ae. aegypti under climate change. However, the extent of spread varies considerably between projections, each under the same Shared Socioeconomic Pathway scenario (SSP3-7.0). For example, by 2100, climatic conditions in London may be suitable for Ae. aegypti for between one and five months in the year, depending on natural climate variability. Our results demonstrate that natural climate variability yields different possible future Ae. aegypti spread scenarios. This affects vector-borne disease risks, including the potential for some regions to experience outbreaks earlier than expected under climate change alone.

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.

The ability of bacteria to resist the effects of antibiotics, or antimicrobial resistance (AMR), is a growing risk to world health, making it more challenging to combat infectious health problems. The growth rate of bacteria is significantly influenced by temperature, particularly temperatures between 35 and 37{degrees}C, often considered the most suitable for the spread of human illnesses. Knowing that a rise in temperature influences bacterial growth rate, contributing to a higher infection rate, it is imperative to unravel and comprehend the association between climate change and the emergence of AMR. We hypothesized that rising temperatures could exacerbate the emergence of AMR in opportunistic and pathogenic bacteria. To test our hypothesis, we investigated the global distribution of AMR and the correlation between AMR and socioeconomic factors, climate change, and air quality in the United States. The study found high resistance rates to common infections such as Methicillin-resistant Staphylococcus aureus (MRSA) and Vancomycin-resistant enterococcus (VRE) infections are prevalent in many countries. In the United States, MRSA-AMR was more common in low-income states with increased poverty rates and poor air quality. The study also found a positive correlation between the rise in temperature over the past 10 years and AMR bacterial infections. The investigation concluded that socioeconomic factors, climate change, and race collectively impact the prevalence of AMR infections. The probability of AMR infection upsurging in the next decade was highest within states with more frequent rises in temperature over the last 10 years. The model predicted that states with at least 1 {degrees}C rise in temperature over the previous 10 years are expected to experience a surge in AMR bacterial infections in coming years. Though the statistical details might vary depending on the data collected in future, the correlation between climate change and the emergence of AMR in bacterial infection is alarming. The study indicates that climate change has an essential, largely unrecognized influence on AMR bacterial infections that warrants additional research. It implies that comprehensive and integrated strategies are needed to address the AMR and climate change challenges.

Extreme weather events can disrupt human behavior and access to health services, possibly affecting the surveillance of respiratory epidemics. Rainfall, in particular, can reduce the uptake of diagnostic testing, delay the detection of infections, and amplify the disadvantages faced by vulnerable populations. We examined this effect using the COVID-19 pandemic as a case study, analyzing daily testing activity in mainland France and New York State between May 15, 2020, and November 1, 2021. COVID-19 testing data were combined with daily rainfall and local socioeconomic indicators. Epidemic trends and weekly seasonality were extracted using time series modeling and incorporated into Poisson regression models to quantify the impact of rainfall on testing rates and to explore its variation with local rainfall profiles and income levels. Rainfall was consistently associated with reduced testing in both France and New York State. Nationally, 25 millimeters of rainfall in one day were linked to an 8-to-9 percent reduction in testing in France and around a 5 percent reduction in New York State. Spatial patterns revealed that decreases were more pronounced in poorer areas, while locations accustomed to frequent light rainfall showed weaker effects. These findings, across diverse geographies and climates, show that rainfall can hinder respiratory epidemic surveillance by reducing testing, and compounds socioeconomic and climatic vulnerabilities. Recognizing and mitigating weather-driven disruptions is essential to strengthen epidemic surveillance and maintain timely detection under a changing climate.

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.

The extent to which urban vegetation improves environmental quality and affects the health of nearby residents is dependent on typological attributes of "greenness", such as canopy area to alleviate urban heat, grass to facilitate exercise and social interaction, leaf area to disperse and capture air pollution, and biomass to absorb noise pollution. The spatial proximity of these typologies to individuals further modifies the extent to which they impart benefits and influence health. However, most evaluations of associations between greenness and health utilize a single metric of greenness and few measures of proximity, which may disproportionately represent the effect of a subset of mediators on health outcomes. To develop an approach to address this potentially substantial limitation of future studies evaluating associations between greenness and health, we measured and evaluated distinct attributes, correlations, and spatial dependency of 13 different metrics of greenness in a residential study area of Louisville, Kentucky, representative of many urban residential areas across the Eastern United States. We calculated NDVI, other satellite spectral indices, LIDAR derived leaf area index and canopy volume, streetview imagery derived semantic view indices, distance to parks, and graph-theory based ecosystem connectivity metrics. We utilized correlation analysis and principal component analysis across spatial scales to identify distinct groupings and typologies of greenness metrics. Our analysis of correlation matrices and principal component analysis identified distinct groupings of metrics representing both physical correlates of greenness (trees, grass, their combinations and derivatives) and also perspectives on those features (streetview, aerial, and connectivity / distance). Our assessment of typological greenness categories contributes perspective important to understanding strengths and limitations of metrics evaluated by past work correlating greenness to health. Given our finding of inconsistent correlations between many metrics and scales, it is likely that many past investigations are missing important context and may underrepresent the extent to which greenness may influence health. Future epidemiological investigations may benefit from these findings to inform selection of appropriate greenness metrics and spatial scales that best represent the cumulative influence of the hypothesized effects of mediators and moderators. However, future work is needed to evaluate the effect of each of these metrics on health outcomes and mediators therein to better inform the understanding of metrics and differential influences on environments and health.

This study used statistically robust regression models to control for a large set of confounders (including county-level time-invariant factors and time trends, regional-level daily variation, state-level social distancing measures, ultraviolet light, and levels of ozone and fine particulate matter, PM2.5) to estimate a reliable rather than simple regression for the impact of weather on the most accurately measured outcome of COVID-19, death. When the average minimum temperature within a five-day window increased by one degree Fahrenheit in spring 2020, daily death rates in northern U.S. counties increased by an estimated 5.1%. When ozone concentration over a five-day window rose by one part per billion, daily death rates in southern U.S. counties declined by approximately 2.0%. Maximum temperature, precipitation, PM2.5, and ultraviolet light did not significantly associate with COVID-19 mortality. The mechanism that may drive the observed association of minimum temperature on COVID-19 deaths in spring months may be increased mobility and contacts. The effect of ozone may be related to its disinfectant properties, but this requires further confirmation.

Faced with the global pandemic of Covid-19, we need to better understand the links between meteorological factors and the virus and investigate the existence of potential seasonal patterns. In the vein of a recent empirical literature, we reassess the impact of weather factors on Covid-19 daily cases in a panel of advanced and emerging countries between January the first and 28th May 2020. We consider 5 different meteorological factors and go further previous studies. In addition, we give a short-run and medium/long-run time perspective of the dramatic outcomes of the pandemic by both considering infected people (short-run) and fatalities (long-run). Our results reveal that the choice of delays and time perspective of the effects of climatic factors on the virus are crucial as well as Covid-19 outcomes can explain the discrepancies in the previous literature. For the first time, we use a dynamic panel model and consider two different kinds of channels between climate and Covid-19 virus: 1) direct/physical factors related to the survivals and durability dynamics of the virus in surfaces and outdoors and 2) an indirect factor through human behaviors and individual mobility - walking or driving outdoors - to capture the impact of climate on social distancing and thus on Covid-19 outcomes. Our model is estimated via two different estimators and persistence, delays in patterns, nonlinearities and numerous specifications changes are taken into account with many robustness checks. Our work highlights that temperatures and, more interestingly, solar radiation - that has been clearly undervalued in previous studies - are significant climatic drivers on Covid-19 outbreak. Indirect effects through human behaviors i.e interrelationships between climatic variables and people mobility are significantly positive and should be considered to correctly assess the effects of climatic factors. Since climate is per se purely exogenous, climate tend to strengthen the effect of mobility on virus spread. The net effect from climate on Covid-19 outbreak will thus result from the direct negative effect of climatic variables and from the indirect effect due to the interaction between mobility and them. Direct negative effects from climatic factors on Covid-19 outcomes - when they are significant - are partly compensated by positive indirect effects through human mobility. Suitable control policies should be implemented to control the mobility and social distancing.