The health effects of climate change are increasingly recognized, but its role in cancer remains insufficiently explored. We aimed to conduct a systematic spatiotemporal analysis to investigate the association between climatic indicators and cancer incidence of all neoplasms as well as major cancer subtypes. We assessed associations between 17 climatic indicators and incidence of all neoplasms and 34 specific cancer subtypes across 204 countries and territories from 1990 to 2021. Region-level data were analyzed using cross-correlation functions (CCF) and Granger causality analysis (GCA) to identify potential directional and lagged associations, with uncertainty addressed through meta-analytic pooling, multiple-testing correction, evaluation across multiple lag structures, and sex-stratified analyses. Global Moran’s I index with Monte Carlo permutation testing was applied to evaluate spatial clustering of significant GCA results. Our analyses revealed complex and dynamic global trends of climatic indicators and cancer incidence rates, with pronounced spatial variability. The CCF and GCA analyses demonstrated mild-to-moderate lagged associations between climatic indicators and cancer incidence, with an average predominant lag of four to five years. Among 595 climate-cancer pairs, GCA identified 105 significant associations, with air pollution (nitrogen dioxide [NO2], fine particulate matter [PM2.5], and ambient ozone pollution) emerging as the category most frequently associated with cancer incidence, while additional associations were found for vapor pressure deficit (VPD), wind speed, and other indicators. Moreover, sex-specific differences and spatial clustering of significant regions were observed. This study provided a panoramic overview of climate–cancer linkages across all neoplasms and 34 cancer subtypes, offering valuable insights into the delayed associations between climate-related indicators and cancer incidence and highlighting the need for sustained attention and climate-informed policy action.
Infectious disease epidemics in complex urban environments often exhibit hybrid diffusion dynamics that combine localized expansion with sporadic long-distance relocation. Traditional spatial statistical models, such as Conditional Autoregressive (CAR) models, typically rely on fixed, adjacency-based spatial weight matrices, creating risks of structural misspecification when transmission pathways deviate from geographic proximity. To address this limitation, this study develops a Bayesian spatiotemporal modeling framework that treats the spatial weight matrix W as an estimable quantity rather than a fixed input, employing an adaptive CAR prior to infer latent diffusion networks directly from routine surveillance data. This reframing allows the model to distinguish expansion-type from relocation-type connectivity links without requiring pre-specified mobility data. The framework is first evaluated through simulation experiments, demonstrating strong capacity to identify the structural distinction between expansion-type and relocation-type diffusion links (Pearson’s correlation > 0.8). We then apply the model to the 2015 dengue outbreak in Tainan, Taiwan. The inferred diffusion network reveals a distinct hybrid topology: the urban core is sustained by a mixture of contiguous expansion links (30
Urbanization is spatially heterogeneous in many sub-Saharan African settings. This complicates community health planning when rural and peri-urban labels are applied informally or updated inconsistently. We assessed whether multiple publicly available geospatial proxies could characterize settlement dynamics and support Community Health Unit (CHU) level classification within the Kaloleni Rabai Health and Demographic Surveillance System (KRHDSS) in coastal Kenya. We conducted a longitudinal ecological analysis across 10 CHUs in Kilifi County with annual observations from 2017 to 2024. Five geospatial proxy indicators of settlement intensity were summarized within CHU boundaries: Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime lights radiance, Sentinel-2 built-up area percentage, WorldPop built-up area percentage, WorldPop population density, and Degree of Urbanization urban percentage. We quantified year-to-year and cumulative change, assessed concordance using pooled descriptive correlations, CHU-level summary correlations, within-CHU centered correlations, and change correlations, and derived an exploratory consensus classification using k-means clustering applied separately to standardized CHU-level median proxy values, followed by cross-proxy vote scoring. Robustness was assessed using alternative temporal summaries, proxy-exclusion analyses, leave-one-CHU-out checks, temporal resampling, joint k-means clustering, and principal component analysis. The analytic dataset comprised 80 CHU-year observations per proxy with no missing raw proxy values. Degree of Urbanization showed a pronounced floor effect with 37 zero observations. Median cumulative percent change from 2017 to 2024 was 62.25
Human mobility is a major determinant of the spatial spread of emerging infectious diseases. In Maï-Ndombe province, Democratic Republic of the Congo, dependence on waterways as the primary transport network, combined with a degraded road infrastructure and marked environmental constraints, creates profound heterogeneity in spatial connectivity between health zones. In this context, the spread of Mpox shows an irregular spatial distribution whose structural mechanisms remain poorly understood, partly due to the scarcity of mobility data and the fragility of surveillance systems. To quantify the intra-provincial spatial connectivity of Maï-Ndombe by simultaneously integrating road and river networks, and to assess its influence on the spread of Mpox between 2022 and 2025. A spatial connectivity analysis based on an enhanced gravity model integrating demographic attractiveness and a distance-cost factor accounting for slope and land use via the Fuzzy-AHP method was combined with a spatialized metapopulation SEIR model. Inter-zone flows, centrality, and accessibility were quantified. The performance of SEIR models with and without connectivity was compared using RMSE, MAE, and precision gain per zone. Flows are strongly concentrated around pivotal zones (Bokoro, Nioki, Mushie), while peripheral areas (Mimia, Oshwe, Kiri) remain structurally isolated, with accessibility provided primarily by river corridors (Gini index = 0.65; CV = 1.52). The integration of connectivity degrades the overall model fit (ΔRMSE = -0.468), reflecting the predominance of local transmission at the provincial scale. However, this aggregate degradation conceals marked spatial heterogeneity: connectivity significantly improves predictions in highly connected zones (Bokoro: +59.8
Malaria remains a significant public health burden in tropical and subtropical regions, where the efficient identification and prediction of risk areas remain challenging. Conventional field surveys used to map Anopheles breeding sites are costly, time consuming and often incomplete. Therefore, there is a pressing need for a geospatially integrated surveillance framework that can accurately map malaria risk and forecast future risk dynamics to support targeted control efforts. A geospatial hybrid modelling framework was developed by integrating multi-source remote sensing, malaria, and climate datasets. A Random Forest model was employed to determine the relative importance of the input variables, which were then weighted and selected for inclusion in a deep-learning architecture. The predictive model combines a 3D Convolutional Neural Network (3DCNN) to capture spatial patterns with a Long Short-Term Memory (LSTM) network to learn temporal dynamics. The model was trained against a baseline mean squared error (MSE) of 0.1 representing a naïve mean predictor. To improve spatial realism of the final risk maps, a Cellular Automata (CA) model was incorporated using a 3 × 3 Moore neighborhood structure, with parameters calibrated at γ = 0.293 and β = 43.9 to enhance the spatial propagation of risk across neighboring cells. The framework successfully mapped malaria risk in Dar es Salaam with a Spearman rank correlation of 0.92, identifying Kigamboni South, Tundwi, and Msongola as high-risk areas. The hybrid 3DCNN-LSTM model reduced the training by 97.3
Abstract Background Global infectious disease surveillance requires timely integration of heterogeneous data sources. Existing platforms typically address only one dimension, leaving cross-domain correlations unexplored. This study presents EpiGIS Pro, an AI-powered geospatial platform that consolidates automated disease event extraction, environmental monitoring, mobility analysis, and predictive analytics within a single web-based framework, providing a shared data infrastructure for future cross-domain modeling. Methods EpiGIS Pro employs a modular multi-application architecture built on Django REST Framework with PostGIS spatial extensions. Disease intelligence is automated from six source categories using Claude AI (Anthropic) for structured extraction with standardized epidemiological metadata. Environmental data, traffic congestion indicators, and international flight route data are ingested via scheduled Celery Beat tasks. Machine learning modules include Prophet-based time-series forecasting with multiplicative seasonality for WHO FluNet data, seasonal Z-score anomaly detection, and effective reproduction number (R_t) estimation. Semantic search is enabled through Voyage AI embeddings stored in pgvector-indexed PostgreSQL. A systematic evaluation framework covers six analytical dimensions across 9 countries spanning 6 years of data. Results The platform integrates 327,000 + records across six data domains. Claude AI extraction processed 353 disease event articles from 56 countries with 92.4% success and 100% completeness for disease type, severity, and priority fields. Using 2,868 WHO FluNet records across 9 countries (2019–2026), seasonality analysis correctly identified hemisphere-concordant peak timing in all 7 evaluated countries. Prophet multiplicative forecasting reduced RMSE by 21–29% compared to the seasonal naive baseline for the United States, and consistently outperformed log-transformed Prophet across all countries and horizons. R_t estimation demonstrated epidemiologically consistent patterns, with onset-period R_t exceeding trough-period R_t in Australia (1.27 vs. 1.09) and Japan (1.80 vs. 1.08). Cross-border risk assessment computed 4,828 flight corridor risk scores by linking disease activity with 633 international aviation routes. Conclusions EpiGIS Pro demonstrates that consolidating AI-driven event extraction, multi-source environmental and mobility data, and geospatial analytics within a unified platform enhances situational awareness for global disease surveillance. The modular architecture and reproducible evaluation framework position EpiGIS Pro as both a practical surveillance tool and a research testbed for computational epidemiology.
BACKGROUND:Childhood obesity remains a pressing public health concern, with significant long-term implications for children's growth and development, as well as an increased risk for various diseases. In recent years, the obesity prevalence among children has shown a concerning rise, particularly in the Deep South (e.g., Louisiana) exhibiting higher rates compared to other regions of the United States. Meanwhile, environmental factors play a critical role in shaping children's health behaviors and outcomes. There has been an increasing body of research on the relation between obesogenic environment and health, but far less focused on child health. METHODS:This study provides a comprehensive measurement of obesogenic environments for children in Louisiana covering park accessibility, walkability, and food environment at the ZIP Code area level. Geographic information system (GIS) analyses revealed significant urban-rural disparities and heterogeneity by race and socioeconomic deprivation. A three-way analysis of variance (ANOVA) further revealed the main and interaction effects of urbanicity, Black population percentage, and area deprivation index (ADI) on obesogenic environment indicators. RESULTS:Regarding the disparities of walkability, not only the individual factors including urbanicity, Black population percentage, and ADI significantly affect walkability, but also the interaction terms among these factors, including both two-way and three-way interactions, are significant. Urban-rural differences and ADI levels contribute to disparities in the food environment. But no significant disparities are observed in park accessibility across different subgroups. CONCLUSION:While park accessibility disparities are limited, the food environment varies significantly by urbanicity and ADI level, walkability shows complex socio-spatial interactions. The study sheds light on targeted public health interventions and urban planning to close the gaps in obesogenic environments for children's health.
Son preference remains a key driver of gender inequality in India, yet most studies treat its determinants as uniform across space, obscuring critical subnational variation. Addressing this gap, this study investigates the geographic heterogeneity of son preference and examines how its predictors vary spatially. Data and methods Using district-level data on 102,045 ever married women aged 15–49 from the National Family Health Survey-5 (2019–2021), we applied a spatially explicit analytical framework, including choropleth mapping, Global Moran’s I, hotspot analysis, Local Indicators of Spatial Association (LISA) cluster mapping, kriging interpolation, and Geographically Weighted Regression (GWR). The use of GWR was justified by significant spatial non-stationarity (Koenker BP = 32.78, p < 0.001) detected in OLS diagnostics. Son preference prevalence ranged 8.2
Abstract Background Specialized pediatric oncology is typically concentrated in a few high-volume centers, creating tensions between the need for centralization and equitable spatial access. For regional health planning, robust methods are required to delineate hospital catchment areas and understand how structural site characteristics and accessibility shape patient-to-hospital travel flows. This study uses pediatric oncology in Bavaria, Germany, as a case to develop and test an extended, empirically calibrated Huff model for modeling hospital catchment areas. Methods We analyzed 3,320 incident cases of pediatric oncology recorded in the German Childhood Cancer Registry between 2014 and 2023, which were treated at the seven specialized hospitals in Bavaria. An extended Huff model was specified that integrates structural indicators of hospital capacity and quality (bed capacity, staffing, cancer center accreditation), a spatial clustering variable that captures proximity-related interactions among nearby hospital sites, and a logistic distance-decay function based on travel times. Model parameters were estimated using maximum likelihood, and competing specifications were compared primarily using mean absolute percentage error (MAPE). A scenario analysis was conducted to assess how a reduction of nurse staffing ratios at two Munich hospitals would affect patient-to-hospital travel flows and catchment areas. Results Our final baseline model, comprising four structural indicators, a clustering variable, and a logistic travel-time function, achieved a MAPE of 5.85% and an R² of 0.89. Capacity and quality indicators displayed positive effects on hospital choice, whereas the clustering parameter was negative, indicating proximity-related interaction effects among nearby hospitals. In the case scenario, a 20% reduction in the nursing staff ratio at the Munich sites led to declining modeled patient shares at both hospitals (− 2.0 and − 2.5% points, respectively) and corresponding gains primarily at Augsburg (+ 3.5% points) and Regensburg (+ 1.3% points), particularly in overlapping and transitional catchment zones. Conclusions Our extended Huff model, which combines multidimensional structural indicators, spatial clustering, and realistic travel-time effects, can accurately represent hospital catchment areas and patient-to-hospital travel flows in specialized pediatric oncology. The approach provides a transparent, empirically grounded framework for assessing accessibility, identifying spatial interdependencies between hospital sites, and conducting scenario-based simulations to inform regional health planning and workforce policy in specialized care settings.
Epidemic forecasting plays a vital role in modern public health. The COVID-19 outbreak underscored the critical need for accurate and responsive models. Recent spatio-temporal Graph Neural Network (GNN) models that integrate human mobility networks face challenges in fully capturing complex, non-linear temporal dynamics and long-range spatial dependencies. To bridge this gap, we introduce two novel spatio-temporal architectures that combine GNNs with Transformer-based temporal modeling: a local-attention-model, which restricts self-attention to temporally adjacent windows of the same node, and a global-attention-model, which leverages full sequence-wide attention across all nodes to capture long-range dependencies. We benchmark our approaches against Persistence, Graph Convolutional Recurrent Network (GCRN) and Graph WaveNet baselines using two real-world datasets from Spain and Brazil. Our models show competitive and superior performance across most metrics compared to recurrent and temporal convolution baselines. The Linear Temporal Graph Convolutional Network (LinearTGCN) variant achieves the best Symmetric Mean Absolute Percentage Error (SMAPE) 24.74% and Mean Directional Accuracy (MDA) 72.52% on Spain dataset, outperforming the full attention-models. While, in Top-40 cities subset of Brazil dataset, local-attention-model slightly matches or outperforms the compared baselines with RMSE (3873.63) and SMAPE (83.47%). Our experiments demonstrate that simple linear models can match or exceed Transformers on structured time series, while Transformers show a great performance on noise or unstructured datasets like Brazil dataset. We found that SMAPE values varies across models by only a few percentage points, while the models are significantly better at directional prediction than the Persistence baseline.
Adequate geographic access to eating disorder treatment is essential for timely care. Yet the distribution of in-person and telehealth programs, and their accessibility across urban and rural settings and across communities with different socioeconomic conditions, remains unclear. We combined an official registry of eating disorder programs from the National Alliance for Eating Disorders with an AI-augmented web corpus built from U.S. domains using ISTARI.AI and large language models. After cleaning and linkage, we analyzed 328 registry centers for in-person care and an augmented set of 2,045 physical sites for proximity checks; telehealth programs were mainly limited to intensive outpatient and partial hospitalization. In-person accessibility was estimated at the census tract level with the two-step floating catchment area method (2SFCA) using program-based capacity. Telehealth accessibility used an unbounded two-step virtual catchment area (u2SVCA) that discounts demand by internet subscription. We also derived tract-level proximity indicators within 30 miles, namely nearest distance and the best available facility size. Population and covariates came from the American Community Survey and the 2020 Census. Multivariable ordinary least squares models related accessibility to median household income, rurality, education, insurance, and race or ethnicity. Correlation-based sensitivity analyses compared metrics across data sources and service regimes that either permit cross-state care or restrict care to within-state. Large language models classified employee size from web summaries with an accuracy of 0.641 and Cohen’s kappa of 0.176. In-person access concentrates in metropolitan corridors, with longer distances and lower accessibility in rural tracts; allowing cross-state care improves proximity near many borders, while within-state constraints reduce reachable capacity across interior states. Telehealth per capita availability varies by state, and effective telehealth access declines after discounting by subscription, with lower values across parts of the South and interior West and the longest mean distances in Alaska. Regression models show strong rural and income gradients. Higher income and urban residence are associated with shorter distance and higher accessibility, while rurality is associated with poorer access for both in-person and telehealth measures. Conditional associations for Black and Latine population shares are small once socioeconomic factors are included. Proximity metrics from the AI-augmented set are moderately correlated with registry-based 2SFCA scores, and telehealth and in-person accessibility show lower but nonzero correlation, suggesting that telehealth and in-person measures capture partially distinct dimensions of potential access, while cross-source differences may also reflect coverage and measurement differences. Eating disorder treatment access remains uneven across the United States. In-person capacity is sparse outside metropolitan regions, and telehealth expands potential reach but remains constrained by broadband subscription and state-based administrative boundaries. Combining registry data with AI-augmented provider information and paired in-person and telehealth accessibility models can help identify communities with limited access. These findings support policy efforts to expand cross-state practice pathways, improve broadband affordability and availability, and increase transparency in program capacity reporting.
The equitable allocation of public service facilities is a key issue in regional coordinated development; however, the long-term spatiotemporal dynamics of their spatial distribution and interregional interaction mechanisms remain underexplored. Based on point-of-interest (POI) data from 13 prefecture-level cities in Jiangsu Province, this study employs exploratory spatial data analysis (ESDA) methods and the spatial Durbin model to investigate the evolutionary patterns and driving mechanisms of sports and recreation facility allocation from 2014 to 2023. The findings reveal that the distribution of sports and recreation facilities in Jiangsu exhibits a stable core-periphery structure, yet limited diffusion is evident, characterized by the sprawling of core areas, the formation of corridors along the Yangtze River, and the emergence of growth poles in peripheral regions. The spatial pattern is undergoing a balanced reconstruction from linear agglomeration to a “from-line-to-area” configuration. Regarding driving mechanisms, the spatial externalities of different factors exhibit significant differentiation: economic growth generates positive synergistic spillovers, while population density, industrial agglomeration, and road density manifest as negative spillovers through siphon effects, crowding-out effects, and consumption substitution, respectively. This dynamic structure helps explain the underlying mechanisms sustaining the highly stable macro pattern. This study is an attempt to reveal the duality of spatial spillover effects of public service facilities with a progressive analytical paradigm. The empirical evidence from Jiangsu Province may provide useful references for the optimization of facility layout and spatial governance in other regions.
Abstract Background Active school travel (AST) can significantly increase children’s physical activity. AST research often utilises global positioning system (GPS) devices to objectively track the routes children take to school. However, GPS-based studies are costly and often subject to recruitment bias which limits population representativeness. Routing engines, such as Google Maps, designed to help people navigate might be low-cost alternatives to the use of GPS if the engines were able to accurately model the routes children took in AST. To date, no studies have systematically evaluated whether routing engines can serve as a viable alternative for modelling children’s walking routes to school. We assessed whether widely available routing engines can approximate children’s walked routes, providing an internationally transferable, low-cost alternative. Methods We compared three routing engines: Google, Mapbox, and Open Source Routing Machine (OSRM), in replicating the GPS measured walking trajectories of 233 school children (age 10–11) across urban and rural Scotland. The percentage of overlap (overlap accuracy, OA), between routing engine predictions and actual GPS tracks was assessed. Results Mean OA was 67.6% for OSRM, 66.8% for Mapbox, and 62.6% for Google, indicating that approximately two-thirds of each predicted route overlapped with the GPS-measured track. These values were substantially higher than a shortest-path baseline computed on the Ordnance Survey (OS) road network (45.0%). No significant differences in OA were found by sex, deprivation, or urban/rural setting. However, route distance was negatively associated with OA ( r = − 0.16 to − 0.23, p < 0.05). At an individual level, one-third of participants showed consistent results across all engines, whilst 49.4% had one discordant engine and 17.2% had mutually inconsistent engines. Conclusions Contemporary routing engines reproduce roughly two-thirds of children’s walked routes to school, without systematic differences by common sociodemographic or contextual factors. Overall, contemporary routing engines can complement GPS in children’s mobility research and practice, offering an empirical reference point for future AST studies. Trial registration Not applicable.
BackgroundChromosomal inversions are important genetic mechanisms that facilitate local adaptation, ecological flexibility, and behavioural variation in mosquito populations. In Anopheles coluzzii, a dominant malaria vector in West Africa, the 2La inversion has been associated with desiccation tolerance, thermal resistance, and insecticide resistance. Despite Nigeria's ecological diversity and substantial malaria burden, the spatial distribution and clinal dynamics of 2La inversion polymorphism in An. coluzzii remain poorly characterized. This study investigated the distribution of 2La inversion karyotypes across major Nigerian ecozones and examined their association with climatic gradients.MethodsLarvae of Anopheles mosquitoes were sampled across 12 states representing Nigeria's southern, central, and northern ecological zones. Species identification was conducted morphologically and confirmed with PCR diagnostics. The 2La inversion was determined using established molecular assays, and allele frequencies were analyzed with respect to ecozone and climatic gradients. Spatial distribution maps and statistical analyses, including correlation and clinal trend assessment, were done in R version 4.4.ResultsA clear geographic structuring of 2La inversion polymorphism was observed in An. coluzzii populations. The 2La/2La homokaryotype was strongly predominant in the northern Sahelian and Sudan savanna ecozones, reflecting adaptation to hot and arid conditions. In contrast, the standard 2La+/2La+ arrangement was predominant in humid southern forest and mangrove regions. The heterokaryotype (2La/2La+) occurred at intermediate frequencies within the central transitional belt, where ecological gradients overlap. Karyotype frequencies exhibited a pronounced latitudinal cline, with heterozygosity peaking in central Nigeria. The spatial patterns indicate that climatic pressures, particularly aridity and humidity, might be a major determinant of inversion distribution in An. coluzzii from Nigeria.ConclusionThis study provides the first detailed nationwide characterization of 2La inversion polymorphism in An. coluzzii across Nigeria's ecological zones. The strong alignment between inversion frequency and eco-climatic gradients highlights the role of chromosomal rearrangements in promoting vector survival and ecological fitness. These adaptive patterns have significant implications for malaria control, as inversion-mediated adaptability may influence resting behaviour, insecticide response, and vector persistence under climate change. Integrating chromosomal inversion surveillance into entomological monitoring frameworks will be essential for designing ecologically tailored vector control strategies in Nigeria.
Abstract Background HIV testing is a critical entry point for prevention and treatment, yet disparities in uptake persist among high-risk populations in Mozambique. This study investigates the prevalence, spatial distribution, and determinants of lifetime HIV testing among high-risk adults aged 15–49 using 2022–2023 DHS data. Methods A cross-sectional analysis of 15,393 high-risk adults was conducted. Descriptive statistics, spatial analyses (Moran’s I, hotspot analysis, Kriging interpolation, SaTScan), and multilevel logistic regression models were applied to identify individual- and community-level factors associated with HIV testing uptake. We estimated the weighted prevalence of lifetime HIV testing uptake. Adjusted odds ratios (AORs) with 95% confidence intervals were reported to measure the association between explanatory variables and HIV testing uptake, while statistical significance was determined using p-values < 0.05. Results Overall, 63.7% of high-risk adults had ever tested for HIV. Testing uptake was higher among females (66%) than males (57%), and among urban (76%) versus rural residents (56%). The highest testing rate was in the 25–34 age group (78%) and lowest among adolescents 15–19 years (31%). Wealth, education, marital status, employment, media exposure, and HIV knowledge positively influenced testing. Significant regional disparities were observed, with southern provinces (Maputo City 87.7%) showing higher uptake than northern and central provinces (Zambezia 44.9%). Spatial analysis confirmed strong clustering (Moran’s I = 0.78, p < 0.001), identifying low-testing hotspots in northern and central rural areas. Multilevel modeling showed individual and community factors explained 62.9% of between-cluster variance, with females (AOR = 2.39), higher education (AOR = 6.44), marriage (AOR = 4), and urban residence (AOR = 1.58) significantly increasing odds of testing. Conclusion HIV testing uptake in Mozambique remains uneven across socio-demographic and geographic groups. Targeted and equity-focused interventions are needed to expand testing among adolescents, men, rural populations, and residents of northern and central provinces. Strengthening community-based testing services, improving health education, and addressing geographic barriers will be essential for accelerating progress toward national HIV control targets
Urbanization-induced urban diseases threaten public health. The World Health Organization launched the Healthy City Initiative in 1986, and China has incorporated it into the Healthy China 2030 strategy. Currently, China faces significant regional development disparities, and issues regarding the development level of the healthy city in China, as well as its spatiotemporal pattern and convergence, remain to be clarified to promote the equalization of health services. Using panel data of Chinese cities from 2011 to 2022, this study constructs an evaluation system covering 4 dimensions and 25 indicators, and applies the entropy weight method to measure the development level of China’s healthy city development. Methods including exploratory spatial data analysis, spatial correlation analysis, trend surface analysis, kernel density analysis, Dagum Gini coefficient, and kernel density estimation are used to reveal the spatiotemporal pattern of China’s healthy city development. The β-convergence model is employed to conduct the convergence test. From 2011 to 2022, the average level of healthy city development increases from 0.149 to 0.269, representing a growth rate of 80.5
BACKGROUND:Rurality and urbanicity are recognized determinants of public health outcomes that influence policy and resource allocation. However, many commonly used methods to classify the rural-urban continuum, such as the Rural-Urban Commuting Area (RUCA) codes, lack applicability in low and middle-income countries (LMICs). This study introduces the Settlement Type and Road Connectivity (STARC) methodology, which offers a standardized and accessible approach to classifying regions along the rural-urban continuum. METHODS:Leveraging open-source software and readily available data, STARC generates detailed maps composed of small hexagonal polygons. Each hexagon unit is assigned one of twenty-four categorical STARC codes based on its estimated population density and community-based road connectivity. Collectively, the hexagon units comprise the base layer STARC map. Additional metrics related to disease transmission can be overlayed onto each STARC hexagon unit in order to perform hotspot analysis via the local Gi* statistic and create transmission zones. All operations within the STARC process have been packaged into a publicly available tool on GitHub. RESULTS:To demonstrate the STARC process, we executed the full STARC methodology at the local, national, and regional level for study areas in Central and Western Africa. Free roaming dog densities were selected as the metric of interest in order to identify hotspots and transmission clusters for dog-mediated rabies. CONCLUSIONS:In our analysis we demonstrate that STARC codes can be used to standardize the rural-urban continuum and better understand the distribution of connectedness of populations. Hotspot analysis of free roaming dogs in several African countries shows that dog populations, a key factor in rabies transmission, are often concentrated in urban and peri-urban areas, many of which span domestic and international boundaries. By providing a dynamic and data-driven approach to understanding the rural-urban landscape, STARC offers a valuable tool for public health interventions in LMICs.
Background This paper addresses the challenge of allocating patients to healthcare facilities with limited capacities during infectious disease outbreaks. The method is based on a hierarchical time-series model to forecast hospital bed demand and a mixed-integer nonlinear mathematical model for allocating patients among a set of regions to minimize disease spread. Methods Our contributions include (1) a new hierarchical time-series model for forecasting hospital bed demand to enhance regional predictive accuracy, (2) a mathematical model that integrates the forecasting model with a Susceptible, Infected, Recovered (SIR) epidemic model to capture metapopulation dynamics and the impact of patient allocation on disease spread across regions, and (3) a sensitivity analysis to assess the importance of the optimization and forecasting parameters on allocation performance. Results The proposed approach is illustrated with a real-data case study from the COVID-19 pandemic in Florida which demonstrates that forecasting performance depends strongly on regional hospital capacity. The hierarchical model performs better in high-capacity regions, while the univariate model is more effective in regions with sparse bed availability. At the state level, both models yield comparable objective function values, but they lead to markedly different spatial distributions of unmet demand.The sensitivity analysis enables us to study the contributions of individual factors and shows that decision-making frequency plays a more critical role. Based on these findings, monthly decision intervals are recommended and forecasting model selection should be tailored to regional capacity. Conclusion The results highlight the practical effectiveness of the proposed approach and its ability to capture trade-offs in patient allocation strategies. By explicitly modeling the additional disease transmissions resulting from patient reallocations across counties, the proposed framework offers actionable insights to support healthcare preparedness and operational decision-making during pandemics.
BackgroundThe increasing use of geographic data about individuals in health and social research raises ethical challenges that extend beyond existing legal frameworks. While regulations such as data protection laws define boundaries, they rarely provide researchers with sufficient practical guidance for addressing geoprivacy risks.MethodsWe developed a structured, reflexive ethical framework tailored for research involving human-centered geographic data. The framework was designed using a lifecycle approach and informed by both a review of existing literature and the expertise of the multidisciplinary author team. It organizes ethical considerations into five research phases: data collection, storage, sharing, analysis, and results dissemination. To enhance usability, we translated these considerations into 60 guiding questions, each assigned an importance level (high, moderate, or low). An ethical review applicability matrix was also introduced to help determine the level of ethical scrutiny required, based on study characteristics such as data type, granularity, linkage potential, and participant vulnerability.ResultsThe framework offers a practical and scalable tool for embedding ethical reflection into research processes. It supports proportionate ethical review by aligning the sensitivity of specific research practices with the corresponding importance of guiding questions. To demonstrate its adaptability, we provide two case studies in the supplementary materials that apply the framework to different research scenarios with varying levels of geoprivacy sensitivity.ConclusionsBy encouraging early and context-aware engagement with ethical risks, this framework safeguards participant dignity, fosters transparency, and advances ethically responsible research involving geographic data. It equips both researchers and ethics committees with a systematic approach for addressing geoprivacy challenges across diverse health and social science contexts.
IntroductionMalaria is one of the world's most serious public health problems and remains a leading health burden in developing countries such as Ethiopia. Large parts of the country, especially lowland areas such as Abaya Woreda, are affected by this disease. The study area is highly endemic for malaria; therefore, identifying vulnerability hotspots and implementing targeted interventions are important for reducing disease burden and saving lives. The present study aims to identify malaria vulnerability hotspot areas in Abaya Woreda, West Guji, Ethiopia, using a comprehensive geospatial approach including GIS and remote sensing techniques.MethodsTo generate a malaria vulnerability map, ten key factors representing climatic, topographic, environmental, and demographic determinants were derived from multi-source datasets, including Landsat 8, SRTM Digital Elevation Model (DEM), GPS field surveys, and CHIRPS precipitation data. The relative weights of these factors were determined using the Analytic Hierarchy Process (AHP) before being integrated via weighted overlay analysis in ArcGIS 10.8.ResultsThe results revealed that 38.3% of the study area is highly vulnerable to malaria. These areas are closely associated with lower elevations, wetlands, water bodies, greater distances from health facilities, and high population density. Furthermore, 49.1% of the area was identified as moderately vulnerable, while only 12.6% exhibited low vulnerability.ConclusionThe findings of this study provide critical, data-driven insights to support stakeholders and policymakers in designing, prioritizing, and implementing targeted, evidence-based malaria control and prevention strategies in Abaya Woreda, thereby significantly enhancing the efficiency and impact of public health interventions.
