Search Results (203)

Generating fine-scale risk maps for mosquito-borne diseases vectors is an essential tool for guiding spatially targeted vector control interventions in urban settings, given the limited public health resources. This study aimed to generate fine-scale risk maps for dengue vectors using routine vector surveillance data collected at the township scale. We integrated monthly township-specific Breteau Index (BI) data from Guangzhou city (2019 to 2020) with covariates extracted from remote sensing imagery and other geospatial datasets to develop an original random forest (RF) model for predicting hotspot areas (BI ≥ 5). We implemented three data resampling techniques (undersampling, oversampling, and hybrid sampling) to improve the model’s performance and evaluate it using the ROC-AUC, Recall, Specificity, and G-means metrics. Finally, we generated a downscaled risk maps for BI hotspot areas at a 1000 m grid scale by applying the optimal model to fine-scale input data. Our findings indicate the following: (1) data resampling techniques significantly improved the prediction accuracy of the original RF model, demonstrating robust spatial downscaling capabilities for fine-scale grids; (2) the spatial distribution of BI hotspot areas within townships exhibits significant heterogeneity. The fine-scale risk mapping approach overcomes the limitations of previous coarse-scale risk maps and provides critical evidence for policymakers to better understand the distribution of BI hotspot areas, facilitating pixel-level spatially targeted vector control interventions in intra-urban areas. Full article

Background/Objectives: Rural public health is significantly impacted by social drivers of health (SDOH), a set of community-level factors, with rural areas facing challenges such as a higher rate of aging population, fewer jobs, lower income, higher mortality, and poor healthcare access. While much research exists on rurality and SDOH, methodological issues remain, including a narrow definition of SDOH that often overlooks the critical location aspect of healthcare. Methods: This study utilized county-level data from the 2020 Agency of Healthcare Research and Quality SDOH database to investigate geospatial variations in healthcare across the spectrum of rurality. This study employed a set of novel spatial–statistical methods: gradient boosting machines (GBM), Shapley additive explanations (SHAP), and multiscale geographically weighted regression (MGWR). Results: The analysis of 262 variables across 1976 counties identified 20 key variables related to rural healthcare. These variables were grouped into three categories: health insurance status, access to care, and the volume of standardized Medicare payments. The MGWR model further revealed both global and local effects of specific healthcare characteristics on rurality, demonstrating that geographically varying relationships were strongly associated with socio-geographical factors. Conclusions: To improve the SDOH in vulnerable rural communities, particularly in Southern states without Medicaid expansion, policymakers must develop and implement equitable and innovative care models to address social determinants of health and access-to-care issues, especially given the potential cuts to public health programs. Full article

Heat waves affect the health and quality of life of older adults, particularly in urban environments. However, there is limited understanding of how extreme temperatures influence their mobility. This research aims to understand the pedestrian mobility patterns of older adults during heat waves in Madrid, analyzing environmental and sociodemographic factors that condition such mobility. Geospatial data from the mobile phones of individuals aged 65 and older were analyzed, along with information on population, housing, urban density, green areas, and facilities during July 2022. Multiple linear regression models and Moran’s I spatial autocorrelation were applied. The results indicate that pedestrian mobility among older adults decreased by 7.3% during the hottest hours, with more pronounced reductions in disadvantaged districts and areas with limited access to urban services. The availability of climate shelters and health centers positively influenced mobility, while areas with a lower coverage of urban services experienced greater declines. At the district level, inequalities in the availability of urban infrastructure may exacerbate the vulnerability of older adults to extreme heat. The findings underscore the need for urban policies that promote equity in access to infrastructure and services that mitigate the effects of extreme heat, especially in disadvantaged areas. Full article

This study investigates the relationships between air quality, social vulnerability, and health outcomes at the census tract-level in Harris County, Texas. Spatial and regression analyses were conducted using sociodemographic data, air quality indicators, including PM2.5, diesel particulate matter (DPM), nitrogen dioxide (NO₂), and ozone, and health metrics, such as coronary heart disease, chronic obstructive pulmonary disease (COPD), asthma, and stroke prevalence. The results indicated variability in sociodemographic challenges, air pollution, and health outcomes. Social vulnerability strongly correlated with increased prevalence of respiratory and cardiovascular diseases, notably COPD, asthma, and stroke. The air quality metrics showed significant geospatial variability: PM2.5 and NO₂ were concentrated centrally near transportation corridors, DPM was elevated near eastern industrial regions, and ozone peaked in western parts of the county, potentially due to atmospheric transport and photochemical processes. PM2.5 exposure significantly correlated with increased cardiovascular and respiratory health outcomes, particularly at elevated concentrations. In contrast, ozone demonstrated a plateauing effect, increasing the health risks but with a diminishing impact at higher concentrations. The correlations between social vulnerability and air quality were modest, suggesting homogenous distributions of PM2.5, NO₂, and DPM across socioeconomically diverse areas, whereas ozone exposure slightly increased with higher social vulnerability. The findings pointed to the complexity of spatial relationships between socioeconomic status, air pollution, and health, highlighting the need for additional monitoring and targeted interventions to improve health outcomes in socio-demographically and economically challenged communities. Full article

(1) Background: Geospatial associations for COVID-19 mortality were estimated using a cohort of 28,128 hospitalized Medicaid patients identified from the 2020–2021 Kentucky Health Facility and Services administrative claims data. (2) Methods: County-level patient information (age, sex, chronic obstructive pulmonary disease [COPD], and mechanical ventilation use [96 hrs. plus]); social deprivation index (SDI) scores; physician and nurse rates per 100,000; and annual average particulate matter 2.5 (PM_2.5) were used as the predictors. Ordinary least-squares (OLS) regression and multiscale geographically weighted regression (MGWR) with the dependent variable, COVID-19 mortality per 100,000, were performed to compute global and local effects, respectively. (3) Results: MGWR (adjusted R²: 0.52; corrected Akaike information criterion [AICc]: 292.51) performed better at explaining the association between the dependent variable and predictors than the OLS regression (adjusted R²: 0.36; AICc: 301.20). The percentages of patients with COPD and who were mechanically ventilated (96 hrs. plus) were significantly associated with COVID-19 mortality, respectively (OLS standardized βCOPD: 0.22; βventilation: 0.53; MGWR mean βCOPD: 0.38; βventilation: 0.57). Other predictors were not statistically significant in both models. (4) Conclusions: A risk of COVID-19 mortality was observed among patients with COPD and prolonged mechanical ventilation use, after controlling for social determinants, the healthcare workforce, and PM_2.5 in rural and Appalachian counties of Kentucky. These counties are characterized by persistent poverty, healthcare workforce shortages, economic distress, and poor population health outcomes. Improving population health protection through multisector collaborations in rural and Appalachian counties may help reduce future health burdens. Full article

Soil potentially toxic element (PTE) contamination remains a global concern, particularly in rural agricultural regions. This study collected 157 agricultural topsoil samples within a rural area in SW China. Combined with multivariate statistical analysis in the compositional data analysis (CoDa) perspective, the PMF model was applied to identify key contamination sources and quantify their contributions. Potential ecological risk assessment and Monte Carlo simulation were employed to estimate ecological-health risks associated with PTE exposure. The results revealed that the main exceeding PTEs (Mercury—Hg and Cadmium—Cd) are rich in urbanized areas and the GFGP (Grain for Green Program) regions. Source apportionment indicated that soil parent materials constituted the dominant contributor (32.48%), followed by traffic emissions (28.31%), atmospheric deposition (21.48%), and legacy agricultural effects (17.86%). Ecological risk assessment showed that 60.51% of soil samples exhibited higher potential ecological risk (PERI > 150), with moderate-risk areas concentrated in the GFGP regions. The elements Cd and Hg from legacy agricultural effects and atmospheric deposition contributed the most to ecological risk. Health risk assessment demonstrated that most risk indices fell within acceptable ranges for all populations, while only children showed elevated non-carcinogenic risk (THI_max > 1.0). Among PTEs, the element As, mainly from traffic emissions, was identified as a priority control element due to its significant health implications. Geospatial distributions showed significant risk enrichment in the GFGP regions (legacy agricultural areas). These findings present associated risk levels in sustainable agricultural regions, providing valuable data to support soil environmental management in regions requiring urgent intervention worldwide. Full article

The expansion of the aquaculture sector offers important economic opportunities but also presents significant challenges, particularly in disease management and prevention. Geographic Information Systems (GISs) have become essential tools for supporting aquatic animal health activities. However, despite their benefits, GISs are still underutilized, particularly in developing countries. To promote the adoption of GISs among aquaculture professionals, a specialized GIS course was developed to improve the prowess of users, equipping them with geospatial analysis skills aimed at epidemiological surveillance and disease response in aquaculture. This study describes a GIS capacity-building initiative developed under the Aquae Strength project. The training approach focuses on the context-specific use of geospatial data and practical applications, and provides a learning environment that fosters autonomy through hands-on, problem-based learning. The program utilizes the open-source QGIS software version 3.28 and incorporates customized materials and exercises based on real-world aquaculture scenarios. The authors hypothesized that the course, due to its cost-effectiveness and use of open-source software, would be particularly beneficial in low- and middle-income settings. The methodological framework described is explicitly designed for easy replication, allowing aquaculture professionals worldwide to download all the course materials and implement similar GIS capacity-building initiatives. The project was funded by the Italian Ministry of Health and supported by the World Organisation for Animal Health (WOAH). It runs from February 2022 to February 2025, with a one-year extension. Full article

Aquifer health assessment is essential for sustainable groundwater management, particularly in semi-arid regions with challenging geological conditions. This study presents a novel methodology for assessing aquifer health in the Barakar River Basin, a hard-rock terrain, by integrating tree-based classification, deep learning, and the Soil and Water Assessment Tool (SWAT) model. Employing Random Forest, Decision Tree, and Convolutional Neural Network (CNN) models, the research examines 20 influential factors, including hydrological, water quality, and socioeconomic variables, to classify aquifer health into four categories: Good, Moderately Good, Semi-Critical, and Critical. The CNN model exhibited the highest predictive accuracy, identifying 33% of the basin as having good aquifer health, while Random Forest assessed 27% as Critical heath. Pearson correlation analysis of CNN-predicted aquifer health indicates that groundwater recharge (r = 0.52), return flow (r = 0.50), and groundwater fluctuation (r = 0.48) are the most influential positive factors. Validation results showed that the CNN model performed strongly, with a precision of 0.957, Area Under the Curve–Receiver Operating Characteristic (AUC-ROC) of 0.95, and F1 score of 0.828, underscoring its reliability and robustness. Geophysical Electrical Resistivity Tomography (ERT) field surveys validated these classifications, particularly in high- and low-aquifer health zones. This study enhances understanding of aquifer dynamics and presents a robust methodology with broader applicability for sustainable groundwater management worldwide. Full article

While multiple federal screening tools have previously been developed for mapping communities facing environmental injustice and health disparities, many states across the United States have seen value in developing state-specific screening tools. This article provides an overview of a recent addition to the list of state screening tools, the Connecticut Environmental Justice Screening Tool (CT EJScreen). CT EJScreen identifies communities disproportionately affected by environmental and socioeconomic burdens at the census tract level. The tool integrates geospatial data on potential pollution sources, exposures, health sensitivities, and socioeconomic factors to produce a cumulative Environmental Justice Index. This article describes the development process of the tool, its methodological framework, the multi-pronged public engagement during the development process, preliminary correlation analyses, lessons learned, and recommendations for future iterations. Spearman correlation and Principal Component Analysis were applied to assess variable relationships and guide indicator refinement. Stakeholder engagement with Connecticut’s environmental justice communities ensured that the tool reflects both quantitative data and lived experiences. CT EJScreen provides important information for policy implementation covering areas such as funding, public health issues, and permitting. The CT EJScreen process might serve as a useful template for other states looking to devise state-specific adjunct screening systems. Full article

Modern cities are rapidly evolving in terms of urban morphology, driven by exponential population growth that accelerates the urbanisation process. The changes in land use have increased urban area and density, intensifying the urban heat island (UHI) effect, which poses one of the biggest threats to human health and well-being, especially in metropolitan regions. One of the most effective strategies to counter urban heat is the implementation of green infrastructure and the use of suitable building materials that help reduce heat stress. However, access to green spaces and the affordability of efficient building materials are not the same among citizens. This paper aims to identify the socio-economic characteristics of communities in Melbourne, Australia, that contribute to their vulnerability to urban heat under local conditions. This study employs remote sensing and geographical information systems (GIS) to conduct a macro-scale analysis, to investigate the correlation between urban heat patterns and socio-economic characteristics, taking into account factors such as vegetation cover, built-up areas, and land use types. The results from the satellite images and the geospatial data reveal that Deer Park, located in the western suburbs of Melbourne, has the highest land surface temperature (LST) at 32.54 °C, a UHI intensity of 1.84 °C, a normalised difference vegetation index (NDVI) of 0.11, and a normalised difference moisture index (NDMI) of −0.081. The LST and UHI intensity indicate a strong negative correlation with the NDVI (r = −0.42) and NDMI (r = −0.6). In contrast, the NDVI and NDMI have a positive correlation with the index of economic resources (IER) with r values of 0.29 and 0.24, indicating that the areas with better finance resources tend to have better vegetation coverage or plant health with less water stress, leading to lower LST and UHI intensity. This study helps to identify the most critical areas in the Greater Melbourne region that are vulnerable to the risk of urban heat and extreme heat events, providing insights for the local city councils to develop effective mitigation strategies and urban development policies that promote a more sustainable and liveable community. Full article

Background: Radiological imaging has improved our insight into how obesity and sarcopenia impacts acute pancreatitis via several measured variables. However, we lack understanding of the association between social determinants of health and these variables within the acute pancreatitis population. Methods: This study included patients at a single tertiary care center between 1 January 2008 and 31 December 2021. Measurements of visceral adiposity (VA), subcutaneous adiposity (SA), the ratio of visceral to total adiposity (VA/TA), and degree of sarcopenia via psoas muscle Hounsfield unit average calculation (HUAC) were obtained on CT scans performed at presentation. Using geocoded patient data, we calculated the social vulnerability index (SVI) from CDC metrics. Descriptive and regression analyses were performed utilizing clinical and radiological data. Results: In 484 patients with 592 acute pancreatitis-related hospitalization, median (IQR) VA was 176 (100–251), SA was 209.5 (138.5–307), VA/TA ratio was 43.5 (32.3–55.3), and HUAC was 51.3 (44.4–58.9). For our primary outcome, geospatial analyses showed a reverse association between VA and SVI with a coefficient of −9.0 (p = 0.04) after adjustment for age, health care behaviors (i.e., active smoking and drinking), and CCI, suggesting residence in areas with higher SVI is linked to lower VA. However, VA/TA, SA, and HUAC showed no significant association with SVI. The SVI subdomain of socioeconomic status had significant association with VA (−39.78 (95% CI: −75.88–−3.70), p = 0.03) after adjustments. For our secondary outcome, acute pancreatitis severity had significant association with higher VA (p ≤ 0.001), VA/TA (p ≤ 0.001), and lower HUAC (p ≤ 0.001). When comparing single vs. recurrent hospitalization patients, there was significantly higher median VA with recurrences (VA-single acute pancreatitis: 149 (77.4–233) vs. VA-recurrent acute pancreatitis: 177 (108–256); p = 0.04). Conclusions: In this study we found that patients residing in more socially vulnerable areas had lower visceral adiposity. This paradoxical result potentially conferred a protective effect against severe and recurrent acute pancreatitis; however, this was not found to be statistically significant. Full article

Malaria is a persistent public health problem, particularly in sub-Saharan Africa where its transmission is intricately linked to climatic factors. Climate change threatens malaria elimination efforts in limited resource settings, such as in the Mberengwa district. However, the role of climate change in malaria transmission and management has not been adequately quantified to inform interventions. This protocol employs a multi-method quantitative study design in four steps, starting with a scoping review of the literature, followed by a multi-method quantitative approach using geospatial analysis, a quantitative survey, and the development of a predictive Susceptible-Exposed-Infected-Recovered-Susceptible-Geographic Information System model to explore the link between climate change and malaria transmission in the Mberengwa district. Geospatial overlay, Getis–Ord Gi* spatial autocorrelation, and spatial linear regression will be applied to climate (temperature, rainfall, and humidity), environmental (Land Use–Land Cover, elevations, proximity to water bodies, and Normalised Difference Vegetation Index), and socio-economic (Poverty Levels and Population Density) data to provide a comprehensive understanding of the spatial distribution of malaria in Mberengwa District. The predictive model will utilise historical data from two decades (2003–2023) to simulate near- and mid-century malaria transmission patterns. The findings of this study will be used to inform policies and optimise the management of malaria in the context of climate change. Full article

The escalation of industrialization has worsened air quality, underscoring the essential need for accurate forecasting to inform policies and protect public health. Current research has primarily emphasized individual spatiotemporal features for prediction, neglecting the interconnections between these features. To address this, we proposed the generative Comprehensive Scale Spatiotemporal Fusion Air Quality Predictor (CSST-AQP). The novel dual-branch architecture combines multi-scale spatial correlation analysis with adaptive temporal modeling to capture the complex interactions in pollutant dispersion and enhanced pollution forecasting. Initially, a fusion preprocessing module based on localized high-correlation spatiotemporal features encodes multidimensional air quality indicators and geospatial data into unified spatiotemporal features. Then, the core architecture employs a dual-branch collaborative framework: a multi-scale spatial processing branch extracts features at varying granularities, and an adaptive temporal enhancement branch concurrently models local periodicities and global evolutionary trends. The feature fusion engine hierarchically integrates spatiotemporally relevant features at individual and regional scales while aggregating local spatiotemporal features from related sites. In experimental results across 14 Chinese regions, CSST-AQP achieves state-of-the-art performance compared to LSTM-based networks with RMSE 6.11–9.13 μg/m³ and R² 0.91–0.93, demonstrating highly robust 60 h forecasting capabilities for diverse pollutants. Full article

La Crosse virus (LACV) is a rare cause of pediatric encephalitis, yet identifying and mitigating transmission foci is critical to detecting additional cases. Neurologic disease disproportionately occurs among children, and survivors often experience substantial, life-altering chronic disability. Despite its severe clinical impact, public health resources to detect and mitigate transmission are lacking. This study aimed to design a Bayesian modelling approach to effectively identify and predict LACV incidence for geospatially informed public health interventions. A Bayesian negative binomial spatio-temporal regression model best fit the data and demonstrated high accuracy. Nine variables were statistically significant in predicting LACV incidence for the Appalachian Mountain Region. Proportion of children, proportion of developed open space, and proportion of barren land were positively associated with LACV incidence, while vapor pressure deficit index, year, and proportions of developed high intensity land, evergreen forest, hay pasture, and woody wetland were negatively associated with LACV incidence. Model prediction error was low, less than 2%, indicating high accuracy in predicting annual LACV human incidence at the county level. In summary, this study demonstrates the utility of Bayesian negative binomial spatio-temporal regression models for predicting rare but medically important LACV human cases. Future studies could examine more granular models for predicting LACV cases from localized variables such as mosquito control efforts, local reservoir host density and local weather fluctuations. Full article

The proliferation of water hyacinth (Eichhornia crassipes) in the Chongón Reservoir, located within the Parque Lago National Recreation Area in Guayaquil, Ecuador, poses significant challenges to the local aquatic ecosystem and water resource management. This study assesses the impact of water hyacinth coverage on evapotranspiration rates over a 20-year period from 2002 to 2022 using remote sensing data and geospatial analysis. The Normalized Difference Vegetation Index (NDVI), derived from Landsat satellite imagery, along with meteorological records, was utilized to model the spatial and temporal dynamics of water hyacinth coverage and its effects on evapotranspiration. Our results indicate that water hyacinth coverage fluctuates significantly between rainy and dry seasons, increasing from covering 10.42% of the reservoir area in 2002 to a peak of 42.33% in 2017 during the rainy seasons. A strong positive correlation ($r=0.92$, $p<0.001$) was found between water hyacinth coverage and net daily water loss due to evapotranspiration. The evapotranspiration rates associated with water hyacinth were significantly higher during the rainy season (mean of 2309.90 mm/year) compared to the dry season (mean of 1917.87 mm/year). These elevated evapotranspiration rates contribute to increased water loss from the reservoir, potentially impacting water availability for municipal and agricultural use. Controlling the spread of water hyacinth is therefore crucial for preserving the reservoir’s ecological integrity and ensuring sustainable water resource management. The findings of this study provide valuable insights for informing management strategies aimed at mitigating the effects of invasive species on freshwater resources and maintaining aquatic ecosystem health. Full article