Geohealth最新文献

Previous research has primarily focused on the impact of climatic variables and air pollution on Hand, Foot, and Mouth Disease (HFMD). However, there remains limited understanding of how air pollution levels modify these relationships across different regions and populations. This study employed a two-stage, multi-city time-series analysis using data from 13 cities in Jiangsu Province (2015–2023) to explore these effects. A multistage analytical approach, including the distributed lag non-linear model, multivariate meta-regression, and attributable risk calculation, was used to quantify the association between climatic variables, air pollutants, and HFMD. Findings indicated that HFMD incidence is closely associated with meteorological conditions, with peak risk at 24.8°C for average temperature and 89.2% for average relative humidity. Low average wind speed and short sunshine hours (SH) also contributed to increased risk. Air pollutants, such as PM_2.5, SO₂, and O₃, significantly modified these associations. For example, PM_2.5 and SO₂ increased HFMD risk at higher temperatures, while O₃ reduced risk. Under low humidity, some pollutants exhibited protective effects, though risk increased with high humidity. NO₂ had the strongest influence in reducing variability, while high PM_2.5 and SO₂ concentrations weakened the protective effects of SH. These findings emphasize the non-linear influence of climatic variables on HFMD risk and suggest that air pollution's modification of these relationships varies by gender, age, and location. This provides important insights for developing targeted, timely public health warnings.

Extreme weather events (EWEs) continue to threaten the health and well-being of populations across the globe. However, risk from drought and floods is not evenly distributed spatially nor are all populations equally at risk for poor health outcomes. Globally, people living with HIV/AIDS (PLHIV) face a particular set of challenges with EWE exposure including increased susceptibility to disease progression from care disruptions and medication adherence, and general population concentration in areas where rainfall is both highly variable and key to economic well-being. To mitigate the impacts of EWE exposure on PLHIV, it is necessary to understand the historical EWE exposure patterns at HIV care clinics. In this paper, we link open-source measures of drought and flood events to clinic locations from the International epidemiology Databases to Evaluate AIDS (IeDEA) network, a longitudinal study of over 2 million people living with and at risk for HIV in 44 different countries around the globe enrolling in HIV care from 2006 to present. Using generalized additive models fit to clinic-level drought and flood exposures, we show how exposures vary across and within countries, model each clinic's probability of exposure to a drought or flood to identify high-risk areas, and describe how this historical exposure record could ultimately be used to identify at-risk populations for a wide variety of study designs. While EWEs occurred at HIV care clinics around the globe, we found that clinic locations in Southern Africa are particularly vulnerable to flood and drought events as compared to other IeDEA clinic regions and locations.

The Ayeyarwady Delta in Myanmar, home to an estimated 12 million people, faces widespread arsenic contamination similar to other Asian deltas namely Bengal, Red River, and Mekong. Arsenic here primarily results from reductive dissolution of iron minerals in anoxic conditions driven by organic carbon. Here, we used digital elevation model (DEM) data to investigate how drainage density and hierarchical recharge pathways influence arsenic distribution, supported by combined data set of 136 wells (81 new, 55 from a prior study)—up to 215 m deep—along a 170 km west-to-east transect across the delta. Findings indicate arsenic hotspots in the mid-central region of the delta, where high drainage density appears to facilitate focused recharge, delivering organic carbon to underlying aquifers. Compared with other deltaic regions across Asia, the Ayeyarwady has fewer high-arsenic wells, with only 21% of our data set exceeding the local 50 μg/l limit. National screening data from 123,962 wells indicate that while only 8% exceed the regulatory limit of 50 μg/l set by Myanmar, 71% exceed the 10 μg/l guideline recommended by the World Health Organization (WHO). This highlights widespread exposure risk not addressed under the current national standard, particularly for rural communities. The observed variability in arsenic concentrations, driven by complex redox dynamics and groundwater flow patterns, indicates that contamination can occur even within short spatial intervals. A blanket-screening program focused on hotspot regions is essential to ensure that at-risk populations are not unknowingly exposed to unsafe drinking water.

Global bottom-up anthropogenic emission inventories show substantial spatial and temporal differences of short-lived pollutant emissions, which results in uncertainties in terms of air quality and human health impacts. In this study, we compare the emissions of trace gases and aerosols for the year 2015 from three different global emission inventories, the Community Emissions Data System (CEDS), the Copernicus Atmosphere Monitoring Service Global Anthropogenic Emissions (CAMS-GLOB-ANT), and Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants version 6b (ECLIPSEv6b). We then employ the Community Atmosphere Model with chemistry version 6.0 within the Community Earth System Model version 2.2.0 to quantify the atmospheric chemistry and air quality impacts from the above three anthropogenic emission inventories, with a focus on PM_2.5 (particulate matter with aerodynamic diameters equal or less than 2.5 μm) and ozone (O₃). Our results indicate that differences between emission inventories are largest for black carbon, organic carbon, ammonia and sulfur dioxide, in terms of global annual total emissions. These differences in emissions across CEDS, CAMS, and ECLIPSEv6b lead to substantial variations in global annual totals and spatial distribution patterns. This study shows that the global annual total PM_2.5-induced premature mortality is three times higher than that from O₃ mortality, indicating that PM_2.5 is the primary contributor compared with O₃. An inter-comparison of global human health impacts from CEDS, CAMS and ECLIPSEv6b indicates that 80% (CEDS), 81.2% (CAMS), and 77.6% (ECLIPSEv6b) of premature deaths due to anthropogenic activities are associated with Asia and Africa continents.

This study investigated the socioeconomic disparities of asthma incidence attributable to ambient particulate matter in aerodynamic diameter ≤2.5 μm (PM_2.5) exposures among schoolchildren in California, U.S. We found that schoolchildren attending public schools in more vulnerable communities, characterized by higher proportions of people of color, low educational attainment, and poverty, experienced elevated PM_2.5 exposures by 2.07–2.96 μg/m³. The disproportionate PM_2.5 exposures were likely driven by higher traffic-related emissions and point-source facility emissions in these communities. Using school-specific PM_2.5 concentrations, student enrollment numbers, and model-estimated (not directly observed) baseline age-specific asthma incidence rates, we calculated that the asthma incidence rate attributable to 2016 PM_2.5 exposures was 562 new cases per 100,000 schoolchildren [95% confidence interval (CI) = 311–854]. In absolute terms (i.e., asthma incidence), it was equivalent to 34,537 PM_2.5-related new asthma cases (95% CI = 19,090–52,493) among all schoolchildren. On average, more vulnerable communities experienced 140 excess new asthma cases per 100,000 schoolchildren (i.e., the difference in average asthma cases per 100,000 schoolchildren between more and less vulnerable groups) across all demographic factors considered. Examining health disparities separately by each demographic factor revealed that race/ethnicity was associated with the largest disparities (209 new cases per 100,000 schoolchildren), followed by educational attainment (128) and poverty (85). Our findings indicate the substantial socioeconomic disparities of asthma incidence attributable to PM_2.5 among schoolchildren in California. Addressing these health disparities could benefit from sustained and long-term emission reduction strategies, such as adopting zero-emission vehicles, which contribute to lower PM_2.5 levels.

Climate change, rapid urbanization, and population growth are increasingly influencing the quality and quantity of surface water resources, especially in vulnerable reservoir systems. This study investigates the spatiotemporal changes in water features and quality of three key drinking water source lakes-Rawal, Simly, and Khanpur (RSK), located in and around Islamabad, Pakistan. Using Level 2 Landsat 5, 7 and 8 satellite data from 1991 to 2020, changes in lake surface area were assessed through the Google Earth Engine (GEE) platform. Thresholding and geospatial analysis in ArcGIS 10.8 were used to extract and visualize water bodies and surface feature changes. The study found that lake surface areas were directly linked to rainfall levels and decreased with rising temperatures especially during 1991, 2000 2010, and 2020. Water quality was assessed using standard laboratory procedures. Notably, higher bacterial counts were recorded during the wet season, indicating increased microbial contamination likely due to surface runoff. Among the heavy metals analyzed (Fe, F, As, Cu, Zn, Mn, Cr, Pb, Ni, B, Cd, P, Hg), only boron (B), nickel (Ni), and chromium (Cr) were detected above background levels, though within permissible limits. The study highlights the significant influence of climatic variables on both the physical extent and microbial quality of drinking water lakes. These findings offer critical insights for policymakers and water resource managers, providing a replicable framework for monitoring and managing similar reservoirs in other climate-sensitive regions.

Lead exposure remains a persistent environmental health threat. Soil contamination is recognized as an overlooked yet critical reservoir of childhood lead exposure due to a legacy of historical lead use in gasoline, paint, and industry. However, it is unclear whether measuring soil lead is an effective way to screen for risk at the community or neighborhood level, nor if soil lead is a significant predictor of elevated blood lead levels (EBLLs) beyond other socioeconomic and physical environment covariates. Building on prior soil sampling and conducting extensive citywide sampling and analysis, we assemble the largest data set of soil lead to date (n = 1,750) in Chicago. Combined with BLL data reported by the Chicago Department of Public Health (CDPH), municipal data, and census data, we investigated the association between soil lead concentrations, predicted BLLs from the EPA's Integrated Exposure Uptake Biokinetic (IEUBK) model, and EBLL from CDPH blood testing among children in Chicago at the community area scale. We present city-scale soil lead and IEUBK risk maps for Chicago. Furthermore, while median household income remains the strongest single predictor of EBLL prevalence in our models, we provide evidence that soil lead independently contributes significant predictive power. Our findings position systematic soil monitoring as a practical tool for primary prevention, complementing existing prevention and intervention strategies and accelerating progress toward safer cities.

Prenatal exposure to heavy metals (HMs) has been the focus of international research. However, current studies tend to examine individual metals in isolation and rely on traditional linear regression models, which may not adequately reflect the complex, non-linear and interactive effects of mixed metal exposure. The aim of this study was to investigate the relationship between maternal mixed urinary HM exposure levels during pregnancy and adverse birth outcomes such as preterm birth (PTB), low birth weight (LBW) and small for gestational age (SGA) infants using advanced machine learning methods. This study was conducted at a tertiary hospital in Guilin, from 2022 to 2023. A total of 489 pregnant women were enrolled. First-trimester urine samples were collected to quantify HM concentrations using Inductively coupled plasma mass spectrometry. Demographic and clinical data were obtained through structured questionnaires. Bayesian Kernel Machine Regression analysis revealed a significant cumulative effect of mixed metal exposure on adverse pregnancy outcomes, with distinct dose-response relationships. The risk of PTB and LBW increased monotonically with higher exposure levels; the adjusted odds ratios were elevated as metal mixture concentrations increased from the 25th to the 75th percentile. In contrast, the association with SGA exhibited a non-monotonic pattern—higher risk at lower exposure levels and a marked decline in risk at higher concentrations. Inorganic arsenic was identified as the primary toxic component in univariate models. Multivariate response modeling demonstrated the joint influence of metal mixtures on adverse outcomes (AUC = 0.697), with no significant statistical interactions between individual metals, as indicated by parallel dose-response curves (p > 0.05).

Climate change intensifies extreme weather, which in turn influences infectious disease transmission. As a dengue fever (DF) hotspot, Guangzhou lacks research on how extreme weather characteristics and spatial factors interact to shape DF patterns. This study analyzed DF distribution in Guangzhou from 2017 to 2019, using a zero-inflated negative binomial spatial lag (ZINB-SAR) regression model to assess the effects of daytime heatwaves (DHW), nighttime heatwaves (NHW) and extreme precipitation (EP) on DF. Results revealed that DF cases were predominantly clustered in central urban areas, with an epidemic season from May to November. The ZINB-SAR model outperformed negative binomial regression and spatial econometric models, with all spatial effect coefficients significantly positive. Analysis of lagged effects showed that each additional DHW event increased DF cases by up to 10.80% (95% CI: 6.22%–15.59%) at a 2-month lag, while NHW events increased DF by 2.73% (95% CI: −1.59%–7.23%). Threshold analysis indicated DHW intensity shifted from promoting to inhibiting DF between 0.66°C and 0.76°C, while NHW intensity transitioned between 0.95°C and 2.28°C. EP demonstrated the strongest effects at a 3-month lag, increasing DF cases by 12.05% (95% CI: 9.03%–15.17%), although its intensity was not statistically significant. Seasonal and spatial variations in DF incidence were evident. In conclusion, DHW and EP impacts were primarily driven by event frequency rather than intensity, whereas NHW effects were more dependent on intensity. These findings highlight the complex spatiotemporal interplay between extreme weather and DF in Guangzhou, providing critical evidence for developing targeted climate-adaptive disease control strategies.

Numerous studies have reported that short-term exposure to fine particulate matter (PM_2.5) is associated with mortality risk; however, results on high-risk populations and regions have been mixed. This study performed a nationwide time-stratified case-crossover study to assess the association between short-term PM_2.5 and mortality in South Korea (2015–2019) by each cause of death and age group. A machine-learning ensemble PM_2.5 prediction model was used to cover unmonitored districts. We estimated the excess mortality and Years of Life Lost (YLL) attributable to PM_2.5 and non-compliance with the 2021 WHO guidelines (>15 μg/m³). We examined the effect modifications by district-level accessibility to green spaces and medical facilities in the living sphere. In the total population, PM_2.5 was positively associated with mortality for non-accidental causes (OR: 1.008 with 95% CI: 1.006–1.010), circulatory diseases (1.007, 95% CI: 1.003–1.011), and respiratory diseases (1.007, 95% CI: 1.001–1.013). Based on the point estimates, the association was generally greater in younger age groups (0–59 or 60–69 years) than in older age groups (70–80 and 80 years or older), and this pattern was pronounced in mortality for cerebrovascular diseases and pneumonia. The excess mortality fraction and YLL due to non-compliance with WHO guidelines were 0.80% and 186,808.52 years. Our findings suggest high risk populations and benefits for establishing stricter PM_2.5 standards and action plans.