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Informal e-waste recycling releases complex mixtures of hazardous substances, including heavy metals that bioaccumulate in exposed populations-especially among e-waste workers. Emerging evidence links these metals to telomere shortening, a key marker of cellular aging and DNA damage which can lead to noncommunicable diseases (NCDs). This study therefore examined the effects of metal exposure on telomere length among e-waste workers in Agbogbloshie compared to non-e-waste workers in Madina. A total of 78 samples (53 e-waste workers and 25 controls), each with three repeated measurements, were selected from the GEOHealth II study and analyzed for telomere length using quantitative polymerase chain reaction (qPCR) technique. Restricted cubic spline (RCS) modeling was employed to assess the association between metal exposure and telomere length. This study observed consistently shorter relative telomere length among the e-waste workers, particularly those involved in burning activities. Lead (Pb) and chromium (Cr) levels were negatively associated with relative telomere length and zinc (Zn) showed a positive association while magnesium (Mg) exhibited a nonlinear relationship with telomere length. The consistently shorter relative telomere length among e-waste workers coinciding with higher concentrations of Cr and Pb implicates the role of metals in telomere shortening. Larger, long-term studies are recommended for future studies.

Fine particulate matter (PM2.5) poses a public health risk, disproportionately impacting low- and middle-income countries (LMICs). In Peru, where ambient concentrations in urban areas significantly exceed the World Health Organization's annual guideline of 5 µg/m3, lack of air pollution monitoring hinders exposure assessment, health effect research, and policy development. Here, we review efforts to create a national database of estimated ambient PM2.5 in other LMICs, and then discuss our efforts in Peru. We highlight the Peru-based NIH-funded GeoHealth Hub's efforts to establish a nationwide low-cost sensor (LCS) network of 176 PurpleAir monitors. We then describe a hybrid approach for modeling ambient PM2.5 exposure across Peru, leveraging data from LCS, satellite remote sensing, chemical transport models, and advanced machine learning methods. The ground-monitoring network includes sensors in both urban (62.5%) and rural (37.5%) areas, in the 24 Regions of the country, set up in collaboration with national environmental agencies. Initial application of our hybrid approach in Lima demonstrated good prediction for the years 2010-2023, with an R² of 0.88 with existing regulatory ground monitors. We are working to extend the model across Peru at a daily level and at a 5-km2 resolution for 2024-2026. The sustainability of these efforts will depend on building local capacity, securing long-term funding, and integrating the LCS network within the current regulatory environmental monitoring network. The hybrid approach offers a scalable solution to address data scarcity and enable high-resolution exposure modeling in Peru and other LMICs.

The spatial resolution of environmental exposure and sociodemographic population data is often mismatched given limited publicly available population data that complies with privacy requirements for individuals. To address this limitation, we developed a novel matching algorithm to construct a synthetic population at the address-level. To demonstrate how our approach can improve environmental justice (EJ) analyses and health impact assessments (HIAs), we examined sociodemographic patterns of residential proximity to major roadways in Greater Boston (Massachusetts) and HIA results, comparing our method with a random address allocation method. The synthetic population was developed at a census tract-level using US Census microdata and combinatorial optimization methods and then downscaled to address-level parcels by matching building attributes to synthetic households. We designated households within 50 m of a major road "high exposure" and households below state median household income "low income".We found misclassification for individual households (21% of the high exposure/low-income households in the matched data set were identified as such in the random allocation data set). We found modest aggregate differences in matched allocation (3.3% of low-income households had high exposure) compared to random allocation (3.4%). In a HIA, the difference between random and matched allocation would be stronger when there is a strong interactive effect between a sociodemographic effect modifier and exposure on the outcome. Address-level exposure assignment based on synthetic populations can provide more significant and nuanced health impact and EJ analyses. Our novel method can be applied to other regions of the US and expanded to other dimensions of population vulnerability. Communities and decision makers often need to identify if there are disparities in the distribution of hazardous exposures and associated health outcomes. To do so requires understanding of both spatial patterns of exposures and of the attributes of exposed populations. While environmental exposure data are available at increasingly higher spatial resolution, data on high‐resolution population sociodemographic characteristics are limited by privacy requirements in the US. To support the investigation of environmental exposures and health outcomes across sociodemographic characteristics at address‐level resolution, we used publicly available US Census data to simulate an address‐level population with sociodemographic information. In a case study looking at proximity to major roadways in Greater Boston (Massachusetts), we compared exposure patterns between our approach and approaches where household attributes were not used for address assignment. We found large differences in how individual households were identified but modest differences in the percent of households identified as high‐exposure and low‐income. We also showed that differences in estimated health impacts would depend on whether there was a strong interaction between the environmental exposure and sociodemographic variable. The methods used to create the address‐level synthetic population can be replicated in other regions of the US using the same census data resources.

This study investigated the relationships between vacant land and key adverse health behaviors, including smoking, insufficient sleep, and no leisure-time physical activity (No LPA), across census tracts in Chicago, Illinois. Using both global regression and geographically weighted regression (GWR), we evaluated whether neighborhood vacant land ratios (VLRs) were associated with the prevalence of these adverse health behaviors and assessed how these associations varied spatially across the city. We found significant spatial clustering in both vacant land and health behavior indicators, and the spatial clustering patterns of neighborhood vacancy and adverse health behaviors were broadly consistent. In global models, higher VLRs were associated with higher prevalence of adverse health behaviors; after accounting for spatially autocorrelated errors, the associations remained robust for smoking and insufficient sleep but were attenuated for No LPA. GWR results further revealed clear spatial non-stationarity, with stronger positive local associations concentrated in low-income neighborhoods on the south and west sides. When overlaid with Healthy Chicago Zones (HCZs), the strong vacancy-behavior associations aligned primarily with the West, Southwest, Near South, and Far South zones, highlighting these HCZs as priority areas where vacancy was most strongly linked to adverse health behaviors. Our findings support theories of neighborhood disorder and spatial inequality, emphasizing that vacant land is a potentially modifiable environmental determinant of health behaviors and calling for tailored interventions that consider local social and economic contexts to improve community health and advance health equity. This study looked at how empty and unused land in Chicago neighborhoods, known as “vacant land,” was related to three adverse health behaviors: smoking, not getting sufficient sleep, and no leisure‐time physical activity. Using neighborhood‐level data across the city, we found that neighborhoods with higher vacant land ratios generally had higher rates of smoking, insufficient sleep, and no leisure‐time physical activity. These patterns were especially strong in low‐income neighborhoods on the south and west sides of the city, where neglected spaces may have increased stress or fear and discouraged healthy routines such as sleeping well and being physically active. In contrast, in wealthier areas, vacant land showed weaker or no clear links to these behaviors. Overall, the findings suggest that addressing vacant land could help reduce adverse health behaviors and advance health equity, particularly in neighborhoods with high vacancy and socioeconomic disadvantage.

Despite the well-known effects of microbiological contamination of drinking water on enteric disease, there is limited epidemiological research investigating the relationship with drinking water quality direct indicators. To investigate the association between drinking water direct indicators and enteric disease. This study used a nationwide case-crossover study design of 46,020 cases of enteric disease between 2015 and 2019. Cases were successfully linked to a public water supply and exposure to E. coli, total coliforms, turbidity and free available chlorine quantified for case and control periods. There were no statistically significant associations found between all enteric, bacterial-only or protozoan-only enteric disease notifications and water quality direct indicators. The presence of E. coli was associated with increased risk of enteric disease in supplies served by surface water (OR 1.23: 95% CI 1.04, 1.46), with known source water risks (OR 1.28: 95% CI 1.08, 1.52) and where the case was exposed to the highest tertile of rainfall (OR 1.27: 95% CI 1.01, 1.58). Major outbreaks of enteric disease can be caused by contamination of public drinking water supplies. Our findings suggest that this mechanism might also be responsible for sporadic cases of enteric disease caused by zoonotic bacterial pathogens. This nationwide case-crossover study investigates the relationship between microbiological water quality and enteric disease in New Zealand. Linking over 46,000 cases (2015-2019) to public water supplies, we found that E. coli presence, particularly in surface water supplies with known source risks and during high rainfall, was significantly associated with increased enteric disease risk. These findings provide novel evidence that routine water quality indicators may predict sporadic enteric infections, reinforcing the need for enhanced microbial monitoring and treatment, especially in high-risk supply zones.

The causative agent of cholera, Vibrio cholerae, is a bacterium native to the aquatic environment and commensal to zooplankton, namely copepods. V. cholerae thrives in warm, moderately saline water and its incidence is strongly influenced by environmental factors, which have proven critical for predictive awareness of cholera by identifying outbreak locations and timing. Susceptible-Infected-Recovered (SIR) models provide useful information for understanding transmission dynamics and epidemic curves of disease outbreaks. Previous such models lacked predictive ability due to limited data in regions where cholera persists. Here, we include climate variability parameters derived from currently available remote sensing data as primary input, allowing greater utility, compared to traditional SIR models. We present models for two African countries where cholera is endemic, Democratic Republic of Congo (DRC) (R 2 = 0.769) and Nigeria (R 2 = 0.756), that incorporate data for temperature, precipitation, and drought index and have been calibrated using weekly cholera case data from 2017 to 2019. Results suggest these models can be used for reasonably accurate retrospective analyses at both country-wide scale for which they were calibrated and modified for smaller spatial extent, including cholera outbreaks in Borno State, Nigeria and North Kivu, DRC. However, results also suggest predicting future epidemic transmission will be challenging due to data limitations in case reporting and intervention strategies. Thus, climate factors should be considered for future SIR modeling efforts, but further advances in data collection are required for these SIR models to become viable predictive tools. The causative agent of cholera, Vibrio cholerae, is strongly influenced by environmental factors, which proved critical for predictive awareness of cholera by identifying outbreak locations and timing. Susceptible‐Infected‐Recovered (SIR) models provide useful information for understanding transmission dynamics of disease outbreaks. Previous such models lacked predictive ability due to limited data in regions where cholera persists. Here, we include climate variability data derived from currently available remote sensing data as primary input, allowing greater utility, compared to traditional SIR models. We present models for two African countries where cholera is endemic, Democratic Republic of Congo (DRC) (R 2 = 0.769) and Nigeria (R 2 = 0.756), that incorporate climate data and are calibrated using weekly cholera case data from 2017 to 2019. Results suggest these models are useful for examining previous outbreaks including those at smaller spatial extent, though with recalibration and intervention parameterization required, including cholera outbreaks in Borno State, Nigeria and North Kivu, DRC. However, results also suggest predicting future epidemic curves will be challenging due to data limitations in case reporting and intervention strategies. Thus, climate factors should be considered for future SIR modeling efforts, but further advances in data collection are required for these SIR models to become viable predictive tools.

In the last decade, wildfires have surged in frequency, as highlighted in the 2024 National Interagency Fire Center report, and continue to rise, making them a worldwide concern due to their environmental and public health impact. Climate change and shifting fire patterns contribute to this growing challenge. This review addresses the complex relationship between wildfires and public health, facilitating informed decision-making in response to this global challenge. Wildfires intricately affect human health, encompassing physical, psychological and social dimensions. Beyond immediate risks like respiratory issues, cardiovascular incidents, and burns, their enduring effects include prolonged exposure to poor air quality, population displacement, disrupted healthcare, psychological trauma and negative economic impacts. As research methods advance, it is vital to systematically review the existing literature to consolidate knowledge, identify gaps, and guide policies and interventions. Our review aims to provide a comprehensive overview of the health consequences linked to wildfires by synthesizing findings from diverse studies. We systematically reviewed 139 peer-reviewed studies published between 1997 and 2023, retrieved from Web of Science, to synthesize evidence on wildfire exposure metrics, health impacts, and population vulnerabilities. We seek to outline the spectrum of health outcomes, explore potential impact mechanisms, and identify vulnerable populations. Additionally, we critically assess study methodologies, evaluate evidence quality, and pinpoint areas requiring further exploration. Wildfires are happening more often and becoming more severe around the world, driven largely by climate change and changing fire behavior. According to the 2024 National Interagency Fire Center report, this trend is expected to continue, posing growing risks to both the environment and public health. This review looks at how wildfires affect human health across short‐ and long‐term timeframes. Immediate health concerns include breathing problems, heart conditions, burns, and injuries. Over time, wildfires can lead to lasting challenges such as poor air quality, displacement, mental health issues, disrupted healthcare access, and economic stress, especially for vulnerable groups. As wildfire risks grow, it's critical to understand their full impact on health to guide effective policies and responses. This review brings together findings from a wide range of studies to clarify what is known, highlight gaps in evidence, and support data‐driven planning. We assess how different studies measure health impacts, identify which populations are most at risk, and outline where further research is needed to strengthen interventions and protect communities.

This study aims to evaluate the global burden of adverse effects of medical treatment (AEMT) using data from the Global Burden of Disease Study (GBD) 2021. Data were extracted from the GBD 2021, covering 204 countries/territories from 1990 to 2021. AEMT was defined using ICD-9 and ICD-10 codes, encompassing complications from medical procedures, treatments, or healthcare exposures. Estimates were categorized into fatal and non-fatal outcomes and stratified by age, sex, year, and covariates, including the Socio-demographic Index (SDI). Mortality-incidence ratios (MIRs), defined as the ratio of mortality calculated by dividing the number of deaths by the total incident cases, were analyzed. In 2021, the global age-standardized prevalence, incidence, disability-adjusted life years (DALYs), and mortality rates of AEMT were 11.48 (95% uncertainty interval [UI], 8.86-14.13), 150.44 (131.19-171.81), 64.19 (51.06-73.11), and 1.53 (1.29-1.68) per 100,000 population, respectively. DALY rates were highest in the early neonatal group (4,789.47 per 100,000 population [95% UI, 3,682.00-5,963.30]), while mortality rates followed a U-shaped pattern across age groups. In 2021, MIRs were highest at both ends of the age range: the early neonatal group (0.58 [95% UI, 0.55-0.58]) and the 95+ age group (0.05 [0.04-0.06]). This pattern was consistent across all SDI quintiles, with higher MIRs observed in lower SDI quintiles. The significantly higher prevalence and incidence rates of AEMT among the older population in high SDI quintiles, compared to lower SDI quintiles, could be attributed to the healthcare overutilization, highlighting the need for policy adjustments.

Soil contamination with toxicants such as lead (Pb) is notoriously patchy and requires considerable effort to map. In the present study of three mining-impacted Peruvian towns, nearly 2,000 students collected and tested 1,500 soil samples using a field kit that combines simulated gastric extraction with visual detection of a purple Pb rhodizonate precipitate. An additional 2,000 soil samples were tested by our team's field staff. The combined results were mapped and compared with total soil Pb concentrations measured by X-ray fluorescence (XRF). The degree of soil contamination varied across sites and within each site. Quality control was provided by reanalyzing a subset of 120 soil samples from the three sites in the laboratory. Overall, total Pb concentrations were highest in Cerro de Pasco with 64% and 30% of samples in the 201-2,000 and 2,001-20,000 mg/kg range, respectively. Total Pb concentrations were somewhat lower in La Oroya with 88% of samples in the 201-2,000 mg/kg range and even lower in Callao with 79% below 200 mg/kg. However, extractable Pb proportions followed an inverse pattern, with the sum of the proportions of medium and high visual readings increasing from 46% to 57% and 69% in Cerro de Pasco, La Oroya, and Callao, respectively. This trend relates more closely to blood-Pb concentrations in children measured at the three sites 2 decades ago than total Pb concentrations. The study demonstrates that student-conducted soil screening can effectively identify Pb contamination patterns while providing valuable science education. Nearly 2,000 high school students used a simple visual test to check their soil for dangerous lead contamination in the mining‐impacted towns of Cerro de Pasco, La Oroya, and Callao in Peru. Their results revealed widespread contamination at all three sites, with geographic patterns within the towns and across the towns that were consistent with blood‐lead measurements in children conducted over 2 decades ago. Beyond identifying contamination, the project taught students valuable science skills and helped raise community awareness about the risks of lead exposure. Students presented their findings to parents and local officials, with some even organizing their own awareness campaign.

The Atoyac river basin is one of the most polluted watershed basins in Mexico. Recent studies have reported the presence of organochlorine pesticides (OCPs) in this highly urbanized region through environmental monitoring, raising concerns about potential health risks, particularly for children and adolescents. We still lack information about its human exposure through biological samples that represent a more realistic measure of OCPs body burden. To evaluate, we compared the serum concentrations of OCPs in children and adolescents living within and outside the Atoyac watershed basin. We included 428 individuals under 20 years old who participated as controls in a population-based study conducted in three central-southern Mexican states (2021-2024). We collected sociodemographic characteristics through face-to-face interviews and obtained serum samples in which we quantified 24 OCPs by gas chromatography. To georeference, we classified the participants as living within or outside the Atoyac basin and compared their respective serum concentrations for those OCPs detected over 10% of samples. We found two heterogeneous spatial distribution patterns of OCPs serum concentrations. HCB, dieldrin, oxychlordane, and endosulfan sulfate were higher in the Atoyac basin, with the two formers being statistically significant. In contrast, p,p'-DDE was significantly higher outside the Atoyac basin. The two patterns of exposure between the two regions emphasized one pattern driven by industry and agriculture, and the second driven by vector-borne disease control. It reinforces the need for regulation and increased monitoring in the Atoyac river basin to provide information about adverse health effects in children and adolescents. The Atoyac River basin is one of the most polluted areas in Mexico. Organochlorine pesticides (OCPs) are harmful chemicals used in the environment, which may pose health risks‐especially to the youth. However, there is little knowledge about how these chemicals are actually getting into people's bodies. Researchers tested blood samples from 428 children under the age of 20. These participants lived either inside or outside the Atoyac basin and were part of a larger study in three states in central‐southern Mexico between 2021 and 2024. The researchers also gathered information about the participants' backgrounds through interviews. We tested the blood for 24 different OCPs and compared the levels between those living in and outside the Atoyac basin. We found that some pesticides‐like HCB and dieldrin‐were higher in those living in the Atoyac basin. Others, like p,p′‐DDE, were higher in people living outside the basin. These results suggest there are different sources of pesticide exposure: in the Atoyac basin, pollution likely comes from industry and farming, while in other areas, it may come from efforts to control disease‐carrying insects. The study highlights the need for stronger pollution controls and monitoring in the Atoyac River basin to protect children's health.

The adjoint of the U.S. EPA's Community Multiscale Air Quality (CMAQ) model is extended for hemispheric scale applications and is used to estimate location-specific health impacts from primary PM2.5, and PM2.5 precursor emissions (NH3, NOX and SO2). We estimate the monetized health burden due to mortality caused by chronic PM2.5 exposure among adults living in the northern hemisphere, using a generalized concentration-response function. The health impact sensitivities show large spatial variability over the northern hemisphere and exhibit a great deal of seasonal variability, especially for inorganic precursor emissions. The largest marginal impacts are seen for NH3 and primary PM2.5. The estimated health impacts for a 10% reduction in emissions reveal a hemispheric burden of 513,700 avoided mortality and monetized health benefits at above 1.2 trillion USD2016. The largest regional contribution to hemispheric mortality is found to be in East and South Asia, particularly China and India (183,760 and 123,440 for a 10% reduction in emissions, respectively). Monetized health burdens are estimated to be highest in China and Europe (∼365 and ∼252 million USD for a 10% reduction in emissions) while it is relatively similar in India (∼175 million USD) as in Canada and the United States (∼177 million USD). Sectoral source contribution analysis demonstrates that the agriculture (19%) and residential (15%) sectors are the largest contributors to the northern hemispheric scale health burden, however, regional differences exist in the results. Examining location- and sector-specific health impacts can inform more effective regulatory measures. Chronic exposure to outdoor PM2.5 is one of the highest mortality risk factors. We used an air quality model to understand how reducing air pollution could improve health across the Northern Hemisphere. We estimated how many deaths could be avoided and the economic value of these health benefits if emissions were reduced. We looked at primary PM2.5 emissions and gases like ammonia, nitrogen oxides, and sulfur dioxide, which can form PM2.5 in the air. Using a sophisticated sensitivity analysis, we linked emissions to health outcomes and economic impacts in specific locations. Our results show that health impacts vary by season and region, and that ammonia and primary PM2.5 emissions cause the greatest harm. Reducing emissions by 10% could prevent more than 500,000 premature deaths each year and save over $1.2 trillion (USD 2016). East and South Asia had the highest number of deaths, while China and Europe faced the greatest economic burden. Emissions from agriculture and residential sources were the biggest contributors to health impacts. Identifying where pollution reductions would bring the most health benefits can support the development of more targeted and effective air quality policies.

Climate change amplifies many threats to human health. Despite advances in understanding climate change dynamics and impacts, there remains a critical gap in translating scientific knowledge into equitable, and community-driven health interventions. The inaugural One Earth, One Health workshop sought to explore this gap through human-centered design exercises involving interdisciplinary researchers from climate and Earth sciences, engineering, epidemiology, microbiology, and environmental health. Although participants did not co-develop solutions with affected communities, they used stakeholder role-playing to guide ideation and lay groundwork for actionable plans. Through these methods, participants identified community needs and proposed prototype solutions to alleviate health threats exacerbated by global environmental change. Prototypes were organized around infectious diseases, extreme weather, and air quality, as illustrative themes rather than an exhaustive set of risks. Key solutions included strategies for anticipatory systems and early warning (e.g., integrating environmental signals with health data), inclusive communication and infrastructure needs for responding to extreme weather events, and integrated platforms visualizing air quality trends to support tailored, context-aware guidance beyond one-size-fits-all alerts. The workshop highlighted opportunities such as leveraging machine learning, Earth observation, and real-time surveillance to protect communities, but also noted barriers including data quality, technological redundancy, privacy, and governance challenges. Additionally, participants emphasized the need for interdisciplinary teams capable of collaborating across sectors, breaking down silos and addressing gaps in training and education. Overall, the workshop illustrates how process-driven, human-centered approaches can help surface user needs and generate testable prototype concepts, while underscoring the importance of direct community partnership for implementation. Climate and other environmental changes amplify threats to human health, such as extreme weather events, infectious diseases, and poor air quality. When trying to understand which hazards and exposures pose the risk to human health, which populations are most vulnerable, and what interventions might be most protective, scientists rely on hypothesis‐driven approaches. Such approaches may not directly reflect the lived experiences or priorities of affected communities. The One Earth, One Health workshop congregated researchers across disciplines to test a method called human‐centered design. Although this workshop did not include direct participation from every key stakeholder groups, participants role‐played as community members, such as healthcare workers, city planners, parents, and concerned citizens, to simulate more inclusive solution development. Participants discussed and co‐developed early‐stage, user‐centered solutions, such as better disease prediction tools, clearer emergency communications, and unified platforms for air quality monitoring and alerts. Although promising, the solutions face multiple challenges, including limitations in data availability, timeliness, and interoperability and technological complexity. The workshop underscored the importance of collaboration and co‐creation as guiding principles for future climate‐health research and intervention design, including the need to engage researchers, policymakers, healthcare workers, and communities in subsequent phases to support practical, equitable, and beneficial outcomes.

Downscaled climate projections provide valuable information needed to better understand the impacts of climate change on health outcomes and to inform adaptation and mitigation strategies at local to regional scales. Because downscaled climate products vary in their representations of fine-scale spatiotemporal patterns, due to of multiple interacting factors, epidemiologic analyses need to consider how differences across downscaling approaches impact projections of health impacts into the future. We evaluate the projected seasonality of coccidioidomycosis in response to projected temperature and precipitation estimated using global climate models from CMIP6 included in California's Fifth Climate Change Assessment, downscaled using two approaches: (a) dynamical downscaling using the Weather Research and Forecasting model; and (b) hybrid statistical downscaling using the Localized Constructed Analogs approach. Our results indicate that by end of century, coccidioidomycosis transmission is projected to start earlier, end later, and last longer across the California endemic region; however, the magnitude of these changes varies by downscaling method. Specifically, LOCA2-hybrid projected a season onset that is 4.2 weeks earlier and an end that is 4.1 weeks later than historical conditions, while the dynamical approach projected a 4 week earlier onset and a 3.8 week later end compared to the historical period. Overall, the LOCA2-hybrid product estimates that the transmission season will last about 0.3 weeks longer than what is projected using dynamical downscaling by end of century. This analysis highlights the sensitivity of coccidioidomycosis seasonality projections to choice of downscaling product, underscoring the need to account for these differences in mitigation and adaptation planning. Downscaled climate data are essential for projecting future local and regional climate‐related health impacts, but multiple downscaling approaches exist and each capture fine‐scale climate patterns differently. We examined how two common approaches for resolving global climate model data into local projections shape predictions of Valley fever seasonality in California. While both approaches suggest the transmission season will lengthen by the end of the century, they differ in how much change they predict. These differences matter because they can influence the timing of health warnings, resource allocation, and adaptation strategies. Our findings highlight the importance of considering climate data methods when using projections to guide climate and health research and planning.

This study assessed changes in the proximity of wildfires to inpatient healthcare facilities in California during the period 2001 to 2023. This retrospective, descriptive spatial analysis of 22 years of wildfire perimeter and healthcare facility data analyzed distances between each inpatient facility and the nearest wildfire perimeter in each year (wildfire-facility distance). Distances were computed on an annual basis using data from the California Department of Health Care Access and Information and CAL-FIRE's Fire and Resource Assessment Program. Temporal changes in wildfire-facility distances over the study timeframe were analyzed via linear modeling and Kruskal-Wallis test. This analysis revealed that distances from inpatient healthcare facilities in California to nearby wildfires are decreasing by an average of 628 feet per year. More facilities are experiencing nearby wildfires. During 2017-2023, there were 53% more inpatient beds within five miles of a wildfire than in 2001-2008. Wildfires are occurring closer to inpatient healthcare facilities in California. An increasing proportion of California's inpatient bed capacity is exposed to nearby wildfires. Policies to reduce risk posed by wildfires, prepare for evacuations, preserve access to healthcare, and ensure safe location of new facilities are urgently needed to ensure the safety of patients and the wellbeing of populations that depend on inpatient healthcare services. Wildfires can cause damage or evacuation of hospitals and other inpatient healthcare facilities. In this study, we analyzed 22 years of data on where wildfires occurred in California in relation to these healthcare facilities. We measured how close the nearest fire was to each facility in each year, and compared changes in these distances over time. Our analysis shows that these nearest wildfires have been getting closer to healthcare facilities by an average of 628 feet per year, and that the number of inpatient beds exposed to a wildfire within a five mile radius has increased by 53% in the most recent third of the data set as compared with the earliest third. We also found that recently built healthcare facilities, particularly nursing homes, have been experiencing a disproportionate level of exposure to nearby wildfires. These findings suggest the need for increased attention to the safe siting of new facilities, readiness of facilities for evacuations, and other risk reduction actions.

Wildfire smoke exposure is associated with a range of adverse health outcomes. People who are incarcerated may be especially vulnerable to smoke exposure because, compared to non-incarcerated people, they lack agency to control their exposure. The goal of this study, within California, is to (a) geographically characterize wildfire-attributable PM2.5 exposure from 2015 to 2020 and (b) to determine whether the burden of wildfire PM2.5 exposure is higher in neighborhoods that contain carceral facilities compared to neighborhoods without carceral facilities. Data on wildfire-attributable PM2.5 was linked to census-tract level counts of incarcerated and non-incarcerated populations. Statewide statistics on wildfire-attributable PM2.5 were calculated for five exposure metrics: (a) number of weeks with wildfire PM2.5 > 5 μg/m3, (b) number of days with non-zero wildfire PM2.5, (c) mean daily wildfire PM2.5 during the peak exposure week, (d) number of smoke waves (defined as ≥2 consecutive days with >15 μg/m3 wildfire PM2.5), and (e) average of the annual mean wildfire PM2.5 concentrations. To spatially compare wildfire PM2.5 exposure among incarcerated people to non-incarcerated neighbors, population-weighted exposure metrics were calculated for each tract containing incarcerated people and compared to surrounding tracts' exposures using non-incarcerated population weights. Across California, census tracts containing incarcerated people had heightened wildfire-attributable PM2.5 exposures and a large proportion of California's incarcerated population (48.5%) resided in tracts in the highest quartile of non-zero wildfire PM2.5 days compared to non-incarcerated people (25.9%). Prisons and jails in areas that have high wildfire smoke exposure levels should improve ventilation capabilities, provide protective equipment and develop preparedness plans. Wildfire smoke exposure is associated with a range of adverse health outcomes. People who are incarcerated may be especially vulnerable to smoke exposure because, compared to non‐incarcerated people, they do not have the ability to make behavioral changes like wearing a mask or reducing time outside to minimize their exposure. One way to quantify wildfire smoke exposure is to measure wildfire‐attributable PM2.5. In this study, we geographically examined wildfire‐attributable PM2.5 from 2015 to 2020 in California and determined differences in exposure comparing incarcerated people to non‐incarcerated people. To examine the frequency, intensity and duration of wildfire smoke exposure, five metrics of wildfire smoke exposure were created across the state, including the measure number of days with non‐zero wildfire PM2.5. Across California, incarcerated people were almost two times as likely (48.5%) as non‐incarcerated people (25.9%) to live in the highest quartile of the exposure metric number of non‐zero wildfire PM2.5 days. Because people who are incarcerated have limited ability to protect themselves from smoke exposure when wildfires occur, this study demonstrates the need to prioritize this population in studies on the health effects of wildfire smoke exposure and to develop plans to protect people who are incarcerated from wildfire smoke exposure.

Marine vibrios, a group of marine bacteria that are opportunist human pathogens, proliferates faster in coastal environments at warmer temperature. Recently, there have been significant concerns of human infection risks during marine beach recreations due to the elevation of seawater temperature in the United States Eastern Seaboard. This study carried out a quantitative microbial risk assessment (QMRA) to estimate health risks associated with V. vulnificus and V. parahaemolyticus infections from water ingestion during recreational activities under varying climate scenarios that predict sea surface temperatures (SSTs) in the next 75 years. Monte Carlo simulations that incorporate Vibrio concentrations at specific SSTs, ingestion dose during recreational activities, and dose-response relationships were applied to predict the probability of infection under varying exposure scenarios. The risk of Vibrio infections along the U.S. Eastern Seaboard was first estimate for the year 2020 based on measured SSTs. The same approach was applied to predict the risk in the year 2100 based on projected SSTs using two IPCC Representative Concentration Pathways (RCP) scenarios: RCP 2.6 and RCP 8.5. The results indicate that the median risks of V. vulnificus and V. parahaemolyticus from recreational activities along the Eastern Seaboard increase significantly from 2020 to 2100, with rises of up to 1,000-fold under RCP 8.5 and up to 100-fold under RCP 2.6. Furthermore, the estimated risks represent a conservative lower-bound of vibrio-related health impacts because wound infection from water contact was not included. This study highlights the growing public health concern and the need for adaptive management strategies. Marine vibrios are a group of marine bacteria that can cause human diseases. These bacteria grow faster at warmer temperature. There have been more reports of Vibrio diseases in the United States Eastern Seaboard in the recent years due to warmer climate. This study estimates health risks from two types of Vibrio due to accidental ingestion of seawater during water recreation at beaches under future warming scenarios over the next 75 years. Analyses showed that the risks increase significantly from 2020 to 2100. Under the worst‐case climate scenarios, the risk rises to 1,000‐fold. Future climate has a greater impact on infection risks in northern coastlines compared to southern coastlines. This study highlights the growing public health concern and the need for adaptive management strategies.

Nearly annually, blooms of the dinoflagellate Karenia brevis form along the southwest Florida coast leading to a variety of negative impacts, including respiratory irritation (RI) in humans. To limit these impacts, NOAA's National Centers for Coastal Ocean Science (NCCOS) developed a RI model to provide beach-goers with a category-based estimate of RI risk at individual beaches along Florida's Gulf and Atlantic coasts. The RI model is based on: (a) K. brevis cell counts collected at individual beaches; (b) high resolution wind direction and speed forecasts and observations; and (c) point-based beach shoreline orientation used to designate onshore and offshore winds. To test the model logic, an analysis of modeled RI was compared to same-day RI reports, based on the frequency of coughs at individual beaches from the Beach Conditions Reporting System (BCRS). Overall, the model proved to be 88% accurate when K. brevis was present along the southwest Florida coastline from 2006 to 2022. In addition, validation efforts confirmed model assumptions, including: (a) reports of higher RI correlate with higher K. brevis cell abundances; and (b) when cells are present, onshore winds lead to a higher risk of RI. However, individual model categories ("low," "moderate") were less robust. Furthermore, BCRS was not a direct measure of toxic aerosol presence, so some coughing (modeled false negatives) may result from other environmental factors. Together, results suggest the RI model accurately predicts "very low" and "high" risk, but that additional research is needed to better capture environmental conditions when RI is "low" or "moderate." Nearly every year red tide (Karenia brevis) blooms along the southwest Florida coast lead to a variety of negative impacts, including respiratory irritation (RI) in humans, such as coughing, sneezing, and eye irritation, which is especially harmful to those with underlying respiratory conditions. In order to inform beachgoers of the potential presence and risk‐level of respiratory impacts associated with red tide, NOAA's National Centers for Coastal Ocean Science developed a RI model. The model uses red tide cell abundances collected by regional partners and community scientists coupled with wind speed and direction at individual beaches. In this study, we tested the RI model, using estimates of RI at impacted beaches by trained lifeguards and beach ambassadors through the Beach Reporting Conditions System. Overall, we found that the model was accurate in predicting a very low or high risk of RI due to red tide, but that additional research is needed to better predict instances of low and moderate RI. Specifically, we found that several of the underlying model assumptions were true, including that there was a higher risk of RI with higher red tide cell abundances, especially when winds were blowing onshore.

The increasing frequency of wildfires in California, fueled by climate change through hotter, drier conditions, poses uncertain public health risks due to repeated wildfire smoke exposure. This study explores the "recovery period," the time between smoke waves, which may offer respite from smoke impacts, including health risks and adaptation demands. We examine trends in wildfire smoke wave frequency, duration, and recovery periods in California from 2006 to 2020, aiming to assess repeated exposures and develop a framework to evaluate associated health risks via recovery periods. We define a smoke wave as two or more consecutive days with wildfire-specific fine particulate matter (PM2.5) > 1 μg/m3, at the census tract level. Recovery periods are calculated as the days between smoke waves, ending with the first wave of 2021. We also examine community characteristics such as income, race, and education. From 2006 to 2010 to 2016-2020, we observed a 60% reduction in recovery periods, an 85% increase in smoke events, and longer durations. Spatial variability was substantial across census tracts, with the greatest reductions in recovery periods in Southern and Central Valley regions. Northern California, with the shortest recovery periods, showed minimal changes. Communities with higher proportions of minority race groups, single female householders, and lower incomes experienced the largest reductions in recovery period length. This study introduces a framework to assess the repeated impacts of smoke waves, highlighting changing spatio-temporal patterns. Incorporating recovery periods into health risk assessments can guide public health strategies to address compounding risks from wildfire smoke. Wildfires in California are becoming more frequent due to climate change, but the risks of repeated exposure to smoke are not well understood. This study examines the “recovery period,” the time between smoke waves when air quality improves. We aimed to understand how often smoke waves occur, how long they last, and how much time people have to recover. Using data from 2006 to 2020, we explored how smoke wave patterns differ across communities, considering factors like race, ethnicity, and socioeconomic conditions. Our findings show that recovery periods have shortened by 60% and the number of smoke events has increased by 85%. Areas in Southern and Central California saw the largest reductions in recovery periods, while Northern California, which already had the shortest recovery times, saw minimal changes. We also found that communities with higher proportions of racial minorities, lower household incomes, and more single female‐headed households experienced the greatest reductions in recovery periods. This study introduces a new approach to understanding the repeated health effects of wildfire smoke and provides insights that can inform public health strategies to address the growing risks from increasingly frequent wildfires.

Pharmaceutically active compounds (PhACs) may enter the food chain through food crops. This study investigates the influence of dissolved PhACs in irrigation water on rice crops and soil, identifying enduring implications on human and soil health. We conducted a field-scale experiment to investigate the accumulation and impacts of two prevalent PhACs, ibuprofen (IBP) and caffeine (CAF), in irrigation water on rice paddies under realistic agronomic conditions. The experiment was carried out in designated subplots with three dosage levels. The results revealed that IBP exhibited higher persistence in the field soil, leading to ∼2 times higher plant uptake than CAF. Most of the introduced contaminants attenuated in soil,root, shoot or degraded naturally, reducing grain accumulation, which ranged from 0.13% to 0.4% and from 0.38% to 1.4% for IBP and CAF, respectively. However, toxic PhAc metabolites were identified in the grains, raising significant concerns. Owing to its higher translocation and grain accumulation, the hazard quotient (HQ) of CAF surpassed 0.1, indicating a potential risk associated with regular dietary intake. The presence of PhACs significantly altered soil microbial enzyme activities, bacterial abundance, and community composition within the soil-plant microbiome, indicating potential long-term impacts on geo-health. In conclusion, the applied PhACs undergo significant attenuation within the field-soil and plant components, lowering grain accumulation; however, the presence of toxic PhAC metabolites in grains and changes in soil bacterial composition indicate potential concerns.