Geospatial Health最新文献

This review assessed the combined impact of poultry production, climate variability, and agricultural environments on human salmonellosis risk across the United States. It considers whether regions with both high poultry production and notable climate variability show amplified infection patterns and whether environmental transmission pathways are becoming more prominent alongside direct poultry exposure. A comprehensive systematic literature review in PubMed was conducted following PRISMA guidelines for studies published between 2011 and 2025 addressing Salmonella in relation to human incidence, poultry processing and environmental exposure. Our search yielded 22 studies that met the inclusion criteria and it included a range of methods such as surveillance, epidemiological modeling, and intervention research across different U.S. regions. The key analytical variables included were serotype diversity, seasonal and regional distribution, antimicrobial resistance, and climate-related environmental transmission. The findings revealed significant geographic overlap between areas of intensive poultry production and high salmonellosis rates, especially in the southern states. A rise in multidrug-resistant serovars, such as S. infantis in poultry products, was found. Seasonal contamination patterns showed chicken cuts peaking in contamination during late winter, in contrast to the summer peak of human cases. We also observed that temperature extremes and heavy precipitation were linked to increased environmental contamination, particularly of water sources, and higher human exposure risk. These conditions also influenced serotype prevalence and the distribution of resistance genes. As a result, there is a need for integrated One Health strategies that should include adaptive poultry management, climate-responsive environmental monitoring with a focus on serotype-specific risk assessment to reduce the overall public health impact of Salmonella.

This study aimed at investigating the association between satellite-based remotely sensed data on particulate matter with diameters less than 2.5 microns (PM2.5), sulphur dioxide (SO2), nitrogen dioxide (NO2) and carbon monoxide (CO) on the one hand, with the incidence of lung cancer in Thailand on the other. Regression analyses on a nationwide dataset comprising 604,460 confirmed cases reported between 2020 and 2023 were conducted using the Spatial Lag Model (SLM) to assess the relationship between the ambient air pollutants and lung cancer incidence. The results revealed that provinces with the highest cancer incidence rates were consistently found to be located in the eastern part of north-eastern Thailand and the far North as well as some provinces in the South. The SLM accounted for a moderate proportion of variance in lung cancer incidence, with R² values ranging from 0.1548 to 0.1755 over the study period. PM2.5 concentrations were positively and significantly associated with incidence rates each year, an effect increasing from 2020 (0.2160, p=0.0075) to 2023 (0.3096, p=0.0102). These findings highlight the potential of satellite-based air quality data, particularly PM2.5 for predicting and monitoring lung cancer incidence, thereby supporting evidence- based public health planning and environmental policy in Thailand. The results add empirical evidence to the growing body of literature demonstrating the public health consequences of ambient air pollution.

Zacatecas is a Mexican state from where there are few studies about biodiversity, venomous ophidians and people's experience of snakebites. In the state, there are 12 species of venomous snakes distributed in three genera: Crotalus, Micruroides and Micrurus, which could represent some risk for the locals. The objective of this study was to make use of Geographic Information Systems (GIS) and programming to determine the relationship between population variables and the number of snakebites registered by the Zacatecas Health Services (SSZ) from 2007 to 2017 at the municipal level. Climatic, social and biological variables were used to gain a better understanding of the situation. It was found that men working in livestock breeding, agriculture, subsistence hunting or mining are more vulnerable, especially if older than 65. The municipalities of Concepción del Oro, Villa de Cos, El Plateado de Joaquín Amaro, Loreto and Ojocaliente exhibit the highest risk, while special monitoring must be conducted in Guadalupe, Fresnillo and Zacatecas due to their high population density, as well as in Valparaíso on account of its rich venomous ophidian fauna. Additionally, it is suggested to carry out preventive actions and detailed data gathering about snakebites to guarantee information quality. This study constitutes the first formal, detailed work about the epidemiological panorama of envenoming caused by the bite of a snake (ophidiotoxicosis) in Zacatecas from which further investigation and modelling may derive.

We tested the feasibility of integrating Actigraph accelerometers (AG), Global Positioning Systems (GPS) and Geographical Information Systems (GIS) to explore the physical activity in 26 healthy adults and 7 post-stroke individuals. The study subjects wore AG and GPS devices for 7 days. Feasibility outcomes were participants' experience of using these devices and data quality regarding i) valid and synchronized data between the AG and GPS; ii) GPS data distribution among participants living in areas characterized by differently developed built environments; and iii) time and intensity of physical activity in and outside the home. There were >10 hours of synchronized data between the devices and the majority (94%) of participants, irrespective of group, did not report any problems using the AG or GPS. Individuals living in low-density built environment had a higher percentage of GPS points closer to the home compared to those living in areas with high-density built environment where GPS scattering occurred. Although methodological challenges regarding scattering and GPS signal loss in densely built environment in urban areas, the results support the overall feasibility of integrating AG, GPS and GIS to investigate physical activity behaviour.

The spatiotemporal distribution of depressive tendencies across China from 2011 to 2022 was investigated using the Baidu Depression Search Index (BDSI). We examined key influencing natural factors, such as water pollution, air pollution, and deforestation, along with economic indicators, such as gross domestic product per capita, disposable income per capita, and health professionals per 10,000 population. Geographical and Temporal Weighted Regression (GTWR) was applied to capture the spatiotemporal heterogeneity of the BDSI determinants. The results revealed significant regional disparities, with the China's eastern region consistently exhibiting the highest values reflecting heightened mental health concerns, while the western region were found to have the lowest. The BDSI trends followed different trajectories, all of which peaked in 2019 before a sharp decline in 2020. Water pollution transitioned from negative to positive influence in the East, while deforestation exhibited regionally variable effects. Air pollution, peaking in 2019 and 2022, demonstrated the highest impact variability. The economic indicators showed complex regional and temporal patterns underscoring the need for tailored interventions. Together, these findings provided critical insights into the intricate interplay between environmental, economic, and healthcare factors in shaping mental health that highlighted the necessity of region-specific policies to mitigate depressive tendencies and enhance public mental well-being. These research results offer targeted recommendations for regionally adaptive mental health strategies across China.

Rocky Mountain Spotted Fever (RMSF) is a potentially fatal tick-borne disease historically prevalent in the eastern and southeastern U.S. Since the early 2000s, there has been a notable rise in RMSF cases in the south-western U.S. Despite the documented role of dogs in tick-borne disease transmission, research on the influence of other factors, such as veterinary care access, climatic conditions and landscape characteristics on RMSF incidence is limited. This study investigated the combined impact of these factors on RMSF using county-level temperature, relative humidity, precipitation, land cover, dog populations and veterinary care access in Arizona from 2006 to 2021. Employing a spatial negative binomial regression model, the study revealed significant associations between veterinary care access, precipitation, relative humidity, shrubland, and RMSF incidence across three models incorporating lagged effects (0-month, 1-month, and 2-month) for climatic variables. A key finding was that counties experiencing higher veterinary care access were more likely to report lower RMSF case counts (incidence rate ratio (IRR): 0.9237). The mean precipitation consistently showed the highest positive IRR (1.8137) across all models, indicating its strong influence. In contrast, relative humidity (IRR: 0.9413) and shrubland presence (IRR: 0.9265) demonstrated significant negative associations with RMSF incidence. These findings underscore the importance of veterinary care access, climatic factors, and land cover in shaping RMSF dynamics, particularly in regions with increasing incidence rates.

Survival analysis consists of a set of statistical methods used to analyse data where the outcome variable is the time until an event occurs. When such data are collected across distinct spatial regions, incorporating spatial information into survival models can be beneficial. A common approach is to apply an intrinsic Conditional Autoregressive (CAR) prior to an area-level frailty term to account for spatial correlation between regions. We extend the Bayesian Cox semi-parametric model by incorporating a spatial frailty term using the Leroux CAR prior. The aim was to improve the model's ability to describe stroke hospitalisations at the Stroke Centre Hospital in Makassar, Indonesia with a focus on understanding the geographic distribution of hospitalisations, Length of Stay (LOS) and factors influencing patient outcomes. The dataset was obtained from medical records of stroke patients admitted to this hospital (April 2021-June 2024). Variables included LOS, discharge outcomes, sex, age, stroke type, uric acid levels, hypertension, hypercholesterolemia, and diabetes mellitus. Our findings indicate that diabetes, stroke type and the presence of hypercholesterolemia significantly influence recovery rates in stroke patients. Specifically, patients with diabetes had lower recovery, while those with hypercholesterolemia and ischemic stroke patients had faster recovery compared to those with haemorrhagic strokes.

Investigating the spatial effects of population mobility on Human Immunodeficiency Virus (HIV) epidemics provides valuable insights for effective disease control. Data on the incidence and prevalence of HIV and socioeconomic factors from 2013 to 2022 across 31 provinces in China were collected. The Baidu migration index was employed to construct inter-provincial population migration matrices for spatial lag models to evaluate spatial spill-overs and influx risks associated with HIV epidemics macroscopically. This study also analysed the impacts of socioeconomic variables, conducted robustness tests for validation, and performed subgroup analysis stratified by HIV incidence levels. Significant spatial autocorrelation of HIV morbidity was confirmed by finding a positive Moran's I. The spatial lag model indicated that when a given province had a 1-unit increase in HIV incidence, its average outflow would cause a 0.7068-unit incidence rate increment in other destination provinces, while every unit increase of HIV incidence in other provinces would induce a 0.7013-unit HIV average incidence rise in the original one when it played the role of destination on average. Furthermore, higher population density and lower educational attainment were associated with elevated HIV incidence (p<0.001). The robustness of the findings was verified, and subgroup analysis indicated that reasons besides population mobility should be given priority consideration in regions with higher HIV incidence. The risks of population mobility related to the HIV epidemic were quantified, highlighting the necessity of developing effective and acceptable HIV prevention and control strategies specifically tailored for migrant populations.

Access to healthcare in border regions is hampered by the very existence of the border and the limitations of cross-border cooperation between healthcare systems. This work examined the status quo of access to inpatient care at a high level of spatial detail and the potential impact of a cross-border cooperation in the Ems- Dollart border Region (EDR), a region located in the northern Dutch-German border area. A cross-border data model of inpatient care for Germany and The Netherlands was created using hospital beds as supply and 1-km² gridded population data as demand. The enhanced the two-step floating catchment area (E2SFCA) algorithm was applied to match supply and demand using road accessibility as intermediary. The model was calculated both for national and cross-border accessibility scenarios, with results standardised against national averages to account for systemic differences between German and Dutch healthcare settings. The resulting maps of spatial access to inpatient care capacity showed that the region has access rates below the national averages, with access rates in The Netherlands showing greater spatial variation than seen in Germany. The border appeared to be less important as cause of low access rates than other factors, such as the presence of the North Sea coast. The model results for cross-border hospital care showed a very local potential with access gains for only 2.2% of the population in the EDR, mostly in The Netherlands. This increase was drawn from wide areas with average and high access rates from both Germany and The Netherlands.

Wastewater-based epidemiology was utilized during the COVID-19 outbreak to monitor the circulation of SARS-CoV-2, the virus causing this disease. However, this approach is limited by the need for additional methods to accurately translate virus concentrations in wastewater to disease-positive human counts. Combined modelling of COVID-19 disease cases and the concentration of its causative virus, SARS-CoV-2, in wastewater will necessarily deepen our understanding. However, this requires addressing the technical differences between disease, population mobility and wastewater models. To that end, we developed an integrated Agent-Based Model (ABM) that facilitates analysis in space and time at various temporal resolutions, including disease spread, population mobility and wastewater production, while also being sufficiently generic for different types of infectious diseases or pathogens. The integrated model replicates the epidemic curve for COVID-19 and can estimate the daily infections at the household level, enabling the monitoring of the spatial patterns of infection intensity. Additionally, the model allows monitoring the estimated production of infected wastewater over time and spatially across the sewage and treatment plant. The model addresses differences between resolutions and can potentially support Early Warning Systems (EWS) for future pandemics.