Achieving universal health coverage for populations of all ages is a key objective of the Healthy China 2030 strategy. However, optimising the supply-demand balance of healthcare facilities and resources to ensure equitable allocation across age groups remains a major challenge. This study examines the spatial equity of healthcare facilities for different age cohorts and explores the factors shaping these spatial patterns. An improved Maximum Accessibility Equality model (Palma-MAE) was applied to evaluate the spatial equity of healthcare facilities. The Standard Deviation Ellipse (SDE) method was used to analyse spatial distribution patterns of healthcare spatial equity across different age groups. In addition, a Multi-Scale Geographically Weighted Regression (MGWR) model was employed to identify the key factors influencing spatial equity in healthcare facilities among different age cohorts. Among all age groups, the 15–59 age group exhibited the highest level of spatial equity in access to healthcare facilities, followed by the 60–64 and ≥ 65 age groups, while the 0–14 age group showed the lowest level. Spatially, areas approaching equity were more widely distributed for the 0–14 and 60–64 age groups, whereas more complex spatial patterns were observed for the 15–59 and ≥ 65 age groups. Demographic, social, and economic factors exerted different influences across age groups. For the 0–14 age group, spatial equity was mainly associated with population density, population ageing, natural population growth rate, educational attainment, per capita disposable income, and fiscal self-sufficiency. For the 15–59 age group, urbanisation rate, educational attainment, per capita disposable income, population ageing, natural population growth rate, and fiscal self-sufficiency were key influencing factors. For the elderly age groups (60–64 and ≥ 65), spatial equity was primarily influenced by the proportion of non-agricultural industries, fiscal self-sufficiency, per capita GDP, population ageing, natural population growth rate, and disposable income. Substantial regional disparities exist in the spatial equity of healthcare facilities across different age groups. Optimising healthcare resource allocation should therefore account for the heterogeneous healthcare needs of different age cohorts shaped by demographic, social, and economic conditions. These findings provide evidence to support more equitable and efficient allocation of public healthcare resources.
Most emerging infectious diseases originate in wildlife populations. As demonstrated by recent epidemics and pandemics, pathogen spillover from zoonotic reservoirs can have devastating public health impacts. Contact between humans and wildlife reservoirs determines spillover risk, but these interactions remain poorly understood. Despite advancements in geolocation technology and its widespread integration into devices available to consumers and researchers, there are significant challenges to collecting fine-scale human movement data in remote areas to assess contact with wildlife. We aimed to evaluate available methods for collecting these data. We applied the findings to identify an optimal method for a case study on pathogen spillover from bats in rural communities of Macenta Prefecture, Republic of Guinea. Among existing methods for collecting location data from humans, we identified two methods as candidates for our case study: (1) a mobile device with the GPSLogger application and (2) a custom-designed wristwatch with geolocation technology. The spatial accuracy, battery life, and user experience of these methods were assessed under field conditions. This preliminary testing was conducted in remote, forested regions of Macenta, Guinea and Malaysian Borneo, which are areas with repeated zoonotic spillover events. Overall, both methods enabled collection of fine-scale spatial data on human movement and proximity to wildlife habitats in remote, forested regions. From initial trials, the custom-designed wristwatch had greater accuracy, had longer battery life, and was reported by participants to be more comfortable to carry than the mobile devices. We demonstrated the utility of these methods for quantifying individual heterogeneities in space use and identifying areas and populations with high risk for human-wildlife contact. This proof-of-concept study provides evidence of the value of custom-designed location tracking watches, through their application in epidemiologic research in rural Guinea. However, mobile devices may be more accessible and suitable in contexts with high mobile phone usage and service coverage. Further research is needed to integrate these movement data with ecological and demographic data to understand how environment and human behaviour shape the dynamics of disease emergence.
Congenital heart diseases (CHD) are the most common congenital anomalies and a leading cause of morbidity and mortality in neonates and children. In Colombia, geographic variation in their notification reflects not only the true distribution of the disease, but also differences in access to prenatal and postnatal diagnosis and the performance of the healthcare system. Understanding these spatial patterns and their determinants is crucial for identifying potential inequities in detection and reporting. A retrospective ecological study (2016–2020) was conducted at the municipal level. CHD cases reported to SIVIGILA were used, with live births from DANE as the denominator. Geographic, sociodemographic, and healthcare access indicators were obtained from SISPRO and Terridata. Spatial analysis (SaTScan) was used to identify clusters of high CHD notification, and the XGBoost algorithm was applied to model cluster membership. The contribution of associated factors was assessed using SHAP values. Between 2016 and 2020, 2.4 notified CHD cases per 1,000 live births were reported in Colombia. Five high-notification clusters were identified, predominantly in the Andean region and in areas with high population density. The XGBoost model predicted cluster membership with an AUC-ROC of 0.89. Altitude, economic and urban dimensions, population density, access to improved water sources, and prenatal care coverage were the most predictive factors. The identified clusters reflect non-random spatial patterns in CHD notification in Colombia, associated with socio-geographic contextual characteristics that suggest a paradox: greater effectiveness of diagnostic, referral, and reporting systems, rather than a higher underlying occurrence of disease, may be contributing to the observed notification patterns and masking the true burden of CHD.
Investigating the drivers of aerosol optical depth (AOD) in basin cities can improve our understanding of atmospheric processes and their responses to complex terrain and environmental changes. However, the nonlinear relationships between AOD and its potential drivers, as well as their spatial variation across different scales, remain poorly understood. The Theil–Sen trend estimator and nonparametric Mann–Kendall test were used to estimate and assess temporal trends in AOD. XGBoost-SHAP was used to identify nonlinear AOD–driver relationships and quantify the relative importance of individual predictors, while multiscale geographically weighted regression (MGWR) was used to assess their spatial heterogeneity and characteristic spatial scales. AOD showed a distinct rise–decline pattern over the study period. The annual mean AOD decreased to 0.409 during 2015–2020, a 42.1
Reliable household sampling frames are frequently unavailable in rapidly growing secondary cities of sub-Saharan Africa, complicating spatially explicit household surveys. Although satellite-assisted methods have improved field logistics, few approaches combine automated frame construction from open geospatial data with systematic validation of how realized samples adhere to the intended spatial design. We developed OpenFootprint-HH-Sampling, a parameterized QGIS workflow that transforms Google Open Building Footprints into spatially explicit sampling frames. Implemented in Ziniaré and Kombissiri, Burkina Faso, the approach combined proportional stratified random sampling by administrative sector with GPS-based mobile navigation. Sampling performance was evaluated through sector-level allocation, exclusive nearest-neighbor matching, nearest-neighbor distance analysis, and Ripley’s L functions. Direct differences between theoretical and realized L(r) functions were quantified using ΔL(r), MAD, MAE, RMSE, correlation coefficients, and R² values. A total of 723 household interviews were completed during August-October 2024. Median displacement between theoretical and realized interview locations was 89.4 m in Ziniaré and 90.3 m in Kombissiri, with larger deviations in some peripheral sectors. Nearest-neighbor analysis showed preserved local spatial structure in 5 of 10. Ripley-based comparisons indicated that both theoretical and realized samples retained clustered spatial configuration, while ΔL(r)-based metrics revealed stronger preservation at the city level than at the sector level. At the city level, theoretical and realized L(r) functions showed very high shape similarity, with R² values of 0.980 in Ziniaré and 0.988 in Kombissiri. Open building footprint data can support reproducible, spatially explicit household sampling where conventional address systems or complete household registers are unavailable. The results demonstrate operational feasibility and substantial fidelity of the realized sample to the intended spatial design, particularly at the city scale, rather than demographic representativeness of the underlying household population. The workflow may be adapted for health, nutrition, disease surveillance, and demographic surveys, provided that context-specific field verification and household-level inclusion checks are incorporated where population inference is required.
