OBJECTIVE: Hepatitis B is a liver disease caused by Hepatitis B virus (HBV) infection and is highly prevalent in China. To better understand the epidemiological characteristics of hepatitis B in China and develop effective disease control strategies, we employed temporal and spatial statistical methods.
METHODS: We obtained HBV incidence data from the Public Health Science Data Center of the Chinese Center for Disease Control and Prevention for the years 2006 to 2018. Using Geographic Information System (GIS) and SaTScan scanning technology, we conducted spatial autocorrelation analysis and spatiotemporal scan analysis to create a map and visualize the distribution of hepatitis B incidence.
RESULTS: While hepatitis B incidence rebounded in 2011 and 2017, the overall incidence in China decreased.In the trend analysis by item, the incidence varies from high to low. The global spatial autocorrelation analysis revealed a clustered distribution, and the Moran index analysis of spatial autocorrelation within local regions identified five provinces as H-H clusters (hot spots), while one province was an L-L cluster (cold spot). Spatial scan analysis identified 11 significant spatial clusters.
CONCLUSIONS: We found significant clustering in the spatial distribution of hepatitis B incidence and positive spatial correlation of hepatitis B incidence in China. We also identified high-risk times and regional clusters of hepatitis B incidence.

The \"SmartCaregivers\" 1.0 mobile application is a beacon of hope for caregivers (CG) in rural areas, often facing limited access to facilities and support. This study, conducted from February to August 2021, aimed to comprehensively analyze the need for developing a database system and a mobile application tailored to enhance caregiver support and resource management for long-term dependent individuals in the rural areas of Maha Sarakham province, Thailand. The research followed a rigorous research and development (R & D) approach, specifically the ADDIE model (analysis, design, development, implementation, and evaluation). Data were collected from 402 caregivers and 10 key informants through surveys and interviews, as well as from 402 caregivers during the implementation and evaluation phases. The application\'s impact was assessed using a quasi-experimental design with a one-group pre-post-test, and its acceptance was evaluated through the technology acceptance model (TAM). The application significantly improved caregivers\' knowledge scores, with a mean increase from 10.49 ± 2.53 to 12.18 ± 2.76 post-intervention. High scores for perceived usefulness (4.36 ± 0.62) and ease of use (4.31 ± 0.59) reassure the audience about the application\'s effectiveness in providing rapid access to health information, aiding decision-making, and improving care coordination. The system quality was also highly rated, with users appreciating the variety of functions and structural design. This potential for transformation and improvement instills hope and optimism for the future of caregiving in rural areas.

Disparities in accessing advanced stroke treatment have been recognized as a policy challenge in multiple countries, including Japan, necessitating priority solutions. Nevertheless, more practical healthcare policies must be implemented due to the limited availability of healthcare staff and financial resources in most nations. This study aimed to evaluate the supply and demand balance of mechanical thrombectomy (MT) and identify areas with high priority for enhancing stroke centers. The target area of this study was Hokkaido, Japan. We adopted the capacitated maximal covering location problem (CMCLP) to propose an optimal allocation without increasing the number of medical facilities. Four realistic scenarios with varying levels of total MT supply capacity for Primary stroke centers and assuming a range of 90 minutes by car from the center were created and simulated. From scenarios 1 to 4, the coverage increased by approximately 53% to 85%, scenarios 2 and 3 had 5% oversupply, and scenario 4 had an oversupply of approximately 20%. When the supply capacity cap was eliminated and 8 PSCs received 31 or more patients, they became priority enhancement targets. The CMCLP estimates demand coverage considering the supply and demand balance and indicates areas and facilities where MT supply capacity enhancement is a priority.

Management of wildlife populations and the creation of conservation programs depend on the evaluation of wildlife habitats. Habitat suitability mapping is a technique typically used to map appropriate environmental factors and assess species existence in different areas. This study aims to map wildlife habitat suitability sites in Former Dhidhessa wildlife sanctuary, Ethiopia, using GIS-based Analytical Hierarchal Process and Weighted Linear Combination Methods. This study used both primary and secondary data sources. Datasets used to collect data include Digital Elevation Model (DEM), Landsat 9 (OLI/TIRS) and population data. Beside, large mammalian species occurrence data obtained from field survey was used. To map wildlife habitat suitability sites in Former Dhidhessa wildlife sanctuary, environmental factors such as proximity of road network, distance to surface water, land use land cover types, slope, population density and topography were used with the integration of species occurrence data recorded from the study area. These environmental factors scaled to common ranges, and assigned appropriate weights. The quantile classification method was utilized to classify suitability index into five zones (unsuitable, less suitable, moderately suitable, suitable, and highly suitable) to produce the map. Accordingly, the model revealed that 18.9 % of the study area is highly suitable, 19.5 % is suitable, 19.9 % is moderately suitable, 19.5 % is less suitable, and 22.2 % is unsuitable for wildlife. About 58.3 % of the study area is currently identified as suitable for wildlife whereas 41.7 % is unsuitable. This showed that the former Dhidhessa wildlife sanctuary is still having large suitable habitats that can support wide ranges of wildlife. Hence, based on the developed preliminary habitat suitability indices and maps, the federal and local governments shall reevaluate the status of former Dhidhessa wildlife sanctuary and develop future conservation and management plans to enhance the conservation of wildlife and their habitats in the area.

BACKGROUND: The dental healthcare private sector in Riyadh city has been growing rapidly over the past few years; however, there is a lack of information on the accessibility and spatial distribution of private dental healthcare facilities (PDHFs) in the area. This study aimed to evaluate the spatial distribution of PDHFs in Riyadh city in relation to population density in each sub-municipality.
METHODS: The current information regarding the number, location, and operability of PDHFs in Riyadh city was obtained from the Ministry of Health. A total of 632 operating PDHFs were included with the precise location plotted on Quantum Geographic Information System software (version 3.32.1, Essen, Germany) using Google Earth. Four levels of buffer zones-1 km, 3 km, 5 km, and >5 km-were determined. The population statistics and mean monthly individual income per district were gathered from Zadd.910ths. Microsoft Excel (version 16.0, Microsoft, Redmond, WA, USA) and RStudio software (version 4.1.3, Posit Software, PBC, Boston, MA, USA) were used for additional data analysis.
RESULTS: There was an overall ratio of one PDHF per 9958 residents in Riyadh city. Olaya and Maather sub-municipalities had the largest PDHF-to-population ratios: (1:4566) and (1:4828), respectively. Only 36.3% of the city\'s total area was within a 1 km buffer zone from a PDHF. There was an overall weak positive correlation between the number of PDHFs and the total area in each sub-municipality (r = 0.29), and the distribution of PDHFs was uneven corresponding to the area (G* = 0.357).
CONCLUSIONS: There was an uneven distribution of PDHFs in Riyadh city. Some areas were underserved while others were overserved in several sub-municipalities. Policy-makers and investors are encouraged to target underserved areas rather than areas with significant clustering to improve access to care.

Recently, volatile organic compounds (VOCs) have been shown to act as precursors of secondary organic particles that react with ultraviolet rays in the atmosphere and contribute to photochemical smog, global warming, odor, and human health risks, highlighting the importance of VOC management. In this study, we measured VOC concentrations in various contexts including industrial and residential areas of Bucheon, Korea, through mobile laboratory and proton-transfer-reaction time-of-flight mass spectrometry methods to determine winter VOC concentrations and visualized the data based on spatial information. Regional characteristics, temperature/humidity, atmospheric conditions, wind speed, traffic volume, etc., during the measurement period of the study site were comprehensively reviewed. For this purpose, global information system (GIS)-based air quality data and various environmental variables were comprehensively reviewed to assess spatial and temporal concentrations in three dimensions rather than in tables and graphs. Among VOCs, the levels of toluene, methanol, and n + i-butene were relatively high, with average concentrations of 48.3 ± 67.2, 34.4 ± 102.7, and 32.6 ± 57.7 ppb, respectively, at the end of the working day. The highest concentrations occurred near the Ojeong Industrial Complex. Mobile pollution sources are also a major driver of VOCs, highlighting the necessity of comprehensively reviewing traffic variables such as road level, estimated traffic volume, and average speed when identifying hotspots of air pollution. GIS-based visualization analysis techniques will improve the efficiency of air quality management.

General pediatricians and those specialized in developmental-behavioral and neurodevelopmental disabilities support children with neurodevelopmental disorders, such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). We identified substantial geographic disparities in pediatrician availability (eg, urban > rural areas), as well as regions with low pediatrician access but high ASD/ADHD prevalence estimates (eg, the US Southeast).

OBJECTIVE: HIV biomedical intervention uptake is suboptimal among Black sexually minoritized men (SMM) and transgender women (TW). Venues where people meet and interact shape HIV-related risk and prevention behaviors. We aimed to construct GPS-defined venue-based affiliation networks and identify the unique set of venues that could maximize reach of HIV biomedical interventions among Black SMM and TW.
METHODS: We used baseline survey and GPS data from 272 Black SMM and TW in the Neighborhoods and Networks (N2) Cohort Study in Chicago, Illinois (2018-2019). We mapped participants\' GPS data to the nearest pre-identified SMM- and TW-friendly venue (n = 222) to construct affiliation networks. Network analyses were performed to identify influential venues that can yield high reach to intervention candidates.
RESULTS: Participants were affiliated with 75.5 % of all pre-identified venues based on GPS data. Two influential venues were identified in the non-PrEP use network, which when combined, could reach 52.5 % of participants not taking PrEP. Participants that could be reached through these two influential venues reported more non-main sex partners than participants not affiliated with either venue (p = 0.049).
CONCLUSIONS: We demonstrate a potential for GPS-defined venue-based affiliation networks to identify unique combinations of venues that could maximize the impact of HIV prevention interventions.

The escalating use of plastics in agriculture, driven by global population growth and increasing food demand, has concurrently led to a rise in Agricultural Plastic Waste (APW) production. Effective waste management is imperative, prompting this study to address the initial step of management, that is the quantification and localization of waste generated from different production systems in diverse regions. Focused on four Southern European countries (Italy, Spain, Greece, and Portugal) at the regional level, the study uses Geographic Information System (GIS), land use maps, indices tailored to each specific agricultural application and each crop type for plastic waste mapping. Furthermore, after the data was employed, it was validated by relevant stakeholders of the mentioned countries. The study revealed Spain, particularly the Andalusia region, as the highest contributor to APW equal to 324,000 tons per year, while Portugal\'s Azores region had the lowest estimate equal to 428 tons per year. Significantly, this research stands out as one of the first to comprehensively consider various plastic applications and detailed crop cultivations within the production systems, representing a pioneering effort in addressing plastic waste management in Southern Europe. This can lead further on to the management of waste in this area and the transfer of the scientific proposition to other countries.

Soybean is an essential crop to fight global food insecurity and is of great economic importance around the world. Along with genetic improvements aimed at boosting yield, soybean seed composition also changed. Since conditions during crop growth and development influences nutrient accumulation in soybean seeds, remote sensing offers a unique opportunity to estimate seed traits from the standing crops. Capturing phenological developments that influence seed composition requires frequent satellite observations at higher spatial and spectral resolutions. This study introduces a novel spectral fusion technique called multiheaded kernel-based spectral fusion (MKSF) that combines the higher spatial resolution of PlanetScope (PS) and spectral bands from Sentinel 2 (S2) satellites. The study also focuses on using the additional spectral bands and different statistical machine learning models to estimate seed traits, e.g., protein, oil, sucrose, starch, ash, fiber, and yield. The MKSF was trained using PS and S2 image pairs from different growth stages and predicted the potential VNIR1 (705 nm), VNIR2 (740 nm), VNIR3 (783 nm), SWIR1 (1610 nm), and SWIR2 (2190 nm) bands from the PS images. Our results indicate that VNIR3 prediction performance was the highest followed by VNIR2, VNIR1, SWIR1, and SWIR2. Among the seed traits, sucrose yielded the highest predictive performance with RFR model. Finally, the feature importance analysis revealed the importance of MKSF-generated vegetation indices from fused images.