Geohealth最新文献

High ambient temperature poses significant health risk globally. However, the relative importance of different exposure pathways leading to health risks remains unclear. For 9 US states during 2016–2018, ED visit records for heat exhaustion and heat stroke (HEAT), fluid and electrolyte imbalance (FEI), volume depletion (VD), and acute kidney injury (AKI) were identified via diagnosis codes. Co-diagnosed Y92 subcodes (Y codes) were used to categorize the patient's location at the time of injury or condition. Logistic regressions were used to estimate nonlinear associations between same-day temperature and Y codes for 11 non-residential versus residential locations among heat-related ED visits, including stratified analyses by patient age, race, and ethnicity. Odds ratios (OR) were calculated between the 95th versus 50th percentile of temperature. Overall, higher temperature was associated with increased risks of ED visits with Y codes for non-residential locations. HEAT ED visits were more likely to have Y codes for streets compared to residential locations (OR:1.68, 95% CI: 1.12–2.51). Similarly, VD visits were more likely to have Y codes for industrial area (OR: 2.68, 95% CI: 1.98–3.63), farms (OR:7.66, 95% CI: 4.05–14.50), recreation areas (OR:2.25, 95% CI: 1.78–2.84), and streets (OR:1.54, 95% CI: 1.39–1.70), but were less likely to have Y codes for public places (OR: 0.89, 95% CI: 0.84, 0.94). Similar associations were observed for FEI and AKI ED visits. Locations associated with higher heat risks may be due to exposure outdoor temperature and activities, supporting the need to develop strategies and interventions that minimize heat exposure in these areas.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder with significant negative health and economic implications for individuals, families and society. This study utilized an individual-level time-stratified case-crossover study design to investigate the relationship between ambient temperatures and PD mortality among the elderly in China. A combination of conditional logistic regression and distributed lag non-linear model was employed to analyze the data, and the mortality burden attributed to ambient temperatures was quantified. The study included a total of 59,397 deceased PD patients aged 60 years and above who died between 2013 and 2020. Findings revealed that the effects of extremely low temperature (−1°C) could persist for up to 14 days, while the impacts of extremely high temperature (30°C) were acute and last for 4 days and showing a significant harvest effect. For the overall population, the high temperatures significantly increased the risk of death, where low temperature did not. A lag0-14 cumulative odds ratios (COR) of extremely low temperature compared to the reference temperature (15°C) was 1.024 (95% CI: 0.971, 1.080). The lag0-14 COR of extremely high temperature was 1.206 (95% CI: 1.116, 1.304). Additionally, high temperatures attributed greater AF of 4.013 (95% eCI: 1.990, 5.894) comparing to low temperatures did of 0.762 (95% eCI: −0.624, 2.017). Significant differences were found across regions. No statistically significant differences were found between the sex and age. This nationwide study provides evidence for tailored interventions in specific regions and populations to reduce temperature-related PD mortality among the elderly in China.

Produced water is a chemically complex waste stream generated during oil and gas development. Roughly four trillion liters were generated onshore in the United States in 2021 (ALL Consulting, 2022, https://www.gwpc.org/wp-content/uploads/2021/09/2021_Produced_Water_Volumes.pdf). Efforts are underway to expand historic uses of produced water to offset freshwater needs in water-stressed regions, avoid induced seismic activity associated with its disposal, and extract commodities. Understanding the potential exposures from current and proposed produced water uses and management practices can help to inform health-protective practices. This review summarizes what is known about potential human exposure to produced water from onshore oil and gas development in the United States. We synthesize 236 publications to create a conceptual model of potential human exposure that illustrates the current state of scientific inquiry and knowledge. Exposure to produced water can occur following its release to the environment through spills or leaks during its handling and management. Exposure can also arise from authorized releases, including permitted discharges to surface water, crop irrigation, and road treatment. Knowledge gaps include understanding the variable composition and toxicity of produced water released to the environment, the performance of treatment methods, migration pathways through the environment that can result in human exposure, and the significance of the exposures for human and ecosystem health. Reducing these uncertainties may help in realizing the benefits of produced water use while simultaneously protecting human health.

The increasing threat of heat stress poses significant risks to human health globally. To quantify heat exposure more effectively, integrated heat stress indicators (HSIs) have been developed to simplify the classification of heat stress severity and assist in public heat warnings. However, their ability to accurately predict daily heat stroke cases has not been fully assessed. In this study, we evaluated the performance of multiple HSIs in forecasting the number of heat stroke-related emergency ambulance dispatches (HT-EADs) across 47 prefectures in Japan and compared their accuracy to models using raw meteorological variables. Our results indicate that, while HSIs simplify the process of assessing heat stress, they generally show lower performances than models based on raw meteorological data. Among the eight HSIs tested, the Wet Bulb Globe Temperature (T_WBG) showed the strongest predictive power, with median R² values of 0.77 and 0.70 for the calibration and validation periods, respectively. However, models incorporating air temperature, relative humidity, wind speed, and solar radiation outperformed T_WBG, achieving R² values of 0.85 and 0.74. We also observed spatial variability in HSI performance, particularly in cooler regions like Hokkaido, where HSIs provided no improvement over temperature alone. Given these findings, we recommend that HSIs be rigorously evaluated with local health data before being used in heat warning systems for specific locations. For predictions requiring high accuracy, raw meteorological variables could be prioritized to ensure greater precision.

The increase in water contaminants threatens ecosystems and human health, underscoring the need for effective water quality (WQ) control. This study assessed the Kakia-Esamburmbur catchment's water sustainability status for drinking purposes by analyzing water samples from the catchment. In-depth physical, chemical, and microbiological investigations were undertaken using a dual approach. This entailed using the Water Quality Index (WQI) for domestic uses and applying a health risk assessment model. The study revealed that turbidity, total viable bacteria, fecal coliforms, and Escherichia coli exceeded WHO, East African, and Kenyan standards (EAS and KEBS) for domestic use and that 50% of samples tested for electrical conductivity also exceeded these guidelines. According to EAS for natural drinking water, 30% of water sources were classified as poor, 20% as very poor, and 50% as unsuitable for domestic use. Overall mean WQI values, based on standards, indicated that the WQ was unfit for domestic use. Nine pesticides associated with antibiotic resistance genes (ARGs) were identified and quantified using Pesticide Human Health Risk Assessment (PHHRA). As a result, 70% of water samples had high levels of carbendazim (CBD), rendering them unsafe for children and infants to consume. This was indicated by a hazard quotient (HQ) > 1 and hazard index (HI) > 1. Statistical evaluations showed geogenic and anthropogenic pressures control hydrogeochemical and microbiological processes in water.

Influenza epidemics, a major contributor to global morbidity and mortality, are influenced by climate factors including absolute humidity and temperature. Climate change is expected to increase the frequency and severity of climate extremes, potentially impacting the duration and magnitude of future influenza epidemics. However, the extent of these projected effects on influenza outbreaks remains understudied. Here, we use an epidemiologic model adapted for temperate and tropical climates to explore how climate variability may affect seasonal influenza. Using climate anomalies derived from historical data, we found that simulated periods of anomalous climate conditions impacted both the projected influenza outbreak peak size and the total proportion infected, with the strongest effects observed when the anomaly was included just before the typical peak. Effects varied by climate: temperate regions showed a unimodal relationship, while tropical climates exhibited a nonlinear pattern. Our results emphasize that the intensity of weather extremes is key to understanding how climate change may affect influenza outbreaks, laying the groundwork for utilizing weather variability as a potential early warning for influenza activity.

Exertional heat illness poses a significant risk for workers, athletes, and military personnel participating in outdoor activities during hot weather. An important component of heat safety is to monitor environmental conditions through heat stress indices like the wet bulb globe temperature (WBGT) and adjust activity as conditions get progressively hotter. Traditionally, on-site (OS) WBGT measurement devices are used, but phone applications (PAs) offering WBGT estimates have emerged as a potential alternative. However, there is little information on how closely PA-derived WBGTs match OS measurements to guide decision-making. This study compared the PA-derived Zelus WBGT estimates with OS measurements from Kestrel 5400 devices and their impact on activity modification categorization. A 2-month observational study collected 1,056 paired (OS and PA) WBGT measurements from 26 high schools across 11 states in the United States and over diverse surfaces (artificial turf 53%, natural grass 44%, others 3%). WBGT values were categorized using regional activity modification thresholds to account for local acclimatization. Our findings indicated that while exhibiting high correlation (r = 0.89), PA WBGTs were on average about 1°C cooler, with differences of 2–3°C at higher WBGTs. Findings were similar for both grass and artificial turf surfaces. Further, significant discrepancies were observed in WBGT-based activity modification categories, with the PA more frequently indicating lower modification categories compared to OS devices, especially in hotter conditions. In light of these findings, the PA requires further validation prior to its adoption as a replacement for OS measurements.

This study presents the interplay between wild bird migrations and global poultry trade in the unprecedented spread of highly pathogenic avian influenza, particularly the H5N1 clade 2.3.4.4b strain, across the world and diverse ecosystems from 2020 to 2023. We theorized the role of migratory birds in spreading pathogens as various wild bird species traverse major flyways between the northern and southern hemispheres. Simultaneously, we analyzed the global poultry trade data to assess its role in H5N1's anthropogenic spread, highlighting how human economic activities intersect with natural avian behaviors in disease dynamics. Lastly, we conducted spatial hotspot analysis to identify areas of significant clustering of H5N1 outbreak points over different bird families from 2003 to 2023. This approach provides a strong framework for identifying specific regions at higher risk for H5N1 outbreaks and upon which to further evaluate these patterns with targeted intervention studies and research into what is driving these patterns. Our findings indicate that both the poultry sector and wild bird migrations significantly contribute to global H5N1 transmission, which helps better understanding of H5N1 transmission mechanisms when combined with ecological, epidemiological, and socio-economic perspectives. The results are intended to inform policy-making and strategic planning in wildlife conservation and the poultry trade to improve public health and animal welfare globally.

Fire activities introduce hazardous impacts on the environment and public health by emitting various chemical species into the atmosphere. Most operational air quality forecast (AQF) models estimate smoke emissions based on the latest available satellite fire products, which may not represent real-time fire behaviors without considering fire spread. Hence, a novel machine learning (ML) based fire spread forecast model, the Fire Intensity and spRead forecAst (FIRA), is developed for AQF model applications. FIRA aims to improve the performance of AQF models by providing realistic, dynamic fire characteristics including the spatial distribution and intensity of fire radiative power (FRP). In this study, data sets in 2020 over the continental United States (CONUS) and a historical California fire in 2024 are used for model training and evaluation. For application assessment, FIRA FRP predictions are applied to the Unified Forecast System coupled with smoke (UFS-Smoke) model as inputs, focusing on a California fire case in September 2020. Results show that FIRA captures fire spread with R-squared (R²) near 0.7 and good spatial similarity (∼95%). The comparison between UFS-Smoke simulations using near-real-time fire products and FIRA FRP predictions show good agreements, indicating that FIRA can accurately represent future fire activities. Although FIRA generally underestimates fire intensity, the uncertainties can be mitigated by applying scaling factors to FRP values. Use of the scaled FIRA largely outperforms the experimental UFS-Smoke model in predicting aerosol optical depth and the three-dimensional smoke contents, while also demonstrating the ability to improve surface fine particulate matter (PM_2.5) concentrations affected by fires.

West Nile virus (WNV) is a zoonotic virus with a mosquito-avian transmission cycle having occasional spillover to mammals. A network analysis of annual log-transformed WNV case numbers (2003–2022) generated four spatially and temporally coherent clusters among 48 U.S. states and six Canadian provinces. Cluster 1 and Cluster 3 were the largest groups corresponding to the Central Flyway and the closely associated Eastern Flyway (with an east-coast subset). Cluster 2 and Cluster 4 corresponded with less-well defined segments of a distinctly different Western Flyway. Thus, clustering can be explained by migratory pathways of terrestrial birds. We investigated avian involvement in the spread of WNV from potential sources in the southern U.S. Analyses revealed consistent patterns in log-transformed case numbers of human WNV. This study highlights the significant role of migratory birds in shaping the spatiotemporal patterns of WNV incidence across North America. However, the observed variability in incidence also likely reflects the interplay of other factors including local environmental conditions, mosquito populations, and regional variations in both migratory and non-migratory bird populations.