International Journal of Health Geographics最新文献

Background: The effect of environmental exposures on self-reported health has gained attention in the literature in recent years. Rising temperatures have become a public health concern due to climate change and urbanisation at a global scale. Findings vary strongly across regions, highlighting the need for further evidence to inform data-driven decisions.

Methods: This study aimed to assess the impact of land surface temperature (LST) on health-related quality of life (HRQOL) among adults in Lima, Peru. We assessed 700 randomly selected adults using structured interviews covering HRQOL, sociodemographic factors, behavioural habits, and non-communicable disease (NCD) prevalence, between December 2023 and January 2024. LST was derived from satellite-based data.

Results: We detected no association between LST and self-reported health for most of the exposure timeframes. However, we observed an association between spring and summer mean temperature for reporting perfect health and reporting problems in the usual activity and anxiety or depression dimension, respectively.

Conclusions: These findings demonstrate the complex interplay of LST as an environmental exposure and HRQOL in urban settings. Insights from this study can optimise public health policies and interventions aimed at promoting healthy behaviours, improving environmental conditions, and enhancing population well-being. Further research should investigate the observed associations across diverse geographic settings and develop targeted strategies to improve HRQOL in the context of rising global temperatures.

Incidence rates of oesophageal cancer (EC), predominantly oesophageal squamous cell carcinoma (ESCC), in areas of China are the highest worldwide. Selenium, a trace element linked to ESCC risk, likely plays a role in ESCC's enigmatic spatial distribution. We investigated the association between soil selenium and EC incidence in China. We conducted a large ecological study using 2016 population-based EC incidence data from 486 cancer registry catchments covering 380 million people and 74,000 EC cases. We assigned mean soil selenium concentrations to each area from geospatial maps. Age-standardized EC incidence rates (ASRs) were computed. We used linear regression models to estimate approximate incidence rate ratios (IRRs) for ASRs across soil selenium quintiles and for areas classified as deficient (≤ 0.2 mg/kg). The distribution of ASRs differed above and below the selenium deficiency threshold (0.2 mg/kg). Above, 100% of ASRs in females and 87% in males were < 15/100,000. Below, 81% of ASRs in females and 36% in males were < 15/100,000, with ASRs having a wide range (0 - 117.5 per 100,000 person-years). Soil selenium-deficient areas were linked to more than twofold increased EC incidence among males (IRR: 2.45; 95% CI: 2.13, 2.81) and threefold among females (IRR: 3.35; 95% CI: 2.67, 4.19). These findings support the hypothesis of selenium's role in the incidence of EC, which may arise from increased susceptibility to the carcinogenic effects of other exposures in selenium-deficient areas. In China, all EC hotspots occur in selenium deficient areas, yet there are selenium deficient areas with low EC rates.

Background: Physical inactivity is a major health risk worldwide, while walking is one of the most accessible forms of exercise that improves public health and supports sustainable urban mobility. Yet the combined and nonlinear effects of the built environments and seasonal climate on exercise walking in high-density cities remain insufficiently explored. This study aims to uncover these relationships and provide insights for health-oriented and climate-adaptive urban planning.

Methods: Crowdsourced walking trajectory data were analyzed for three representative high-density Chinese cities, Beijing, Wuhan, and Guangzhou, covering both summer and winter. A comprehensive variable system was established, incorporating built environments, seasonal climate, and socioeconomic factors. A geographically weighted extreme gradient boosting model was developed with Bayesian optimization and cross-validation to improve robustness. Interpretability was achieved through Shapley Additive Explanations, partial dependence plots, and clustering analysis to identify global and local drivers of walking activity.

Results: The geographically weighted extreme gradient boosting model outperformed traditional regression and other machine learning models in prediction accuracy. Walking trajectories showed clear spatial clustering, with central urban cores as hotspots, and seasonal differences most pronounced in Beijing. Walk Score was consistently the most stable and influential factor across cities and seasons. Among climatic variables, air quality and temperature had the strongest impacts, particularly in winter. Variables exhibited three types of nonlinear responses: sustained growth (such as Walk Score and pedestrian street length), threshold-sensitive (such as intersection density and population density), and fluctuating patterns (such as air quality and housing prices). Local cluster analysis revealed three context-specific patterns: environment-driven areas such as parks and campuses, function-driven commercial centers, and structurally imbalanced or transitional zones.

Conclusions: Exercise walking in high-density cities is shaped by both seasonal climate variability and spatial heterogeneity of the built environments. Improving pedestrian infrastructure, managing density thresholds, and implementing climate sensitive design can mitigate adverse weather impacts and foster year-round walking. Tailored strategies, including enhancing microclimate resilience in ecological zones, optimizing density and functional mix in commercial districts, and restructuring fragmented large blocks, are essential to create pedestrian friendly, health oriented, and climate adaptive cities.

Background: Minimum caseload requirements (MCRs) ensure medical treatment quality but may negatively affect spatial accessibility to health care. Previous studies optimised the caseload distribution via spatial models, with a focus on balancing spatial concentration and accessibility with centralised case redistribution models. This study seeks to capture hospitals as active participants in MCR policy decisions by incorporating their intentions and motivations regarding MCRs within their spatial context, considering current caseloads and neighbouring hospital distances.

Methods: The study modelled four MCR procedures separately in an individual model in accordance with the German policy context: complex oesophageal interventions, complex pancreatic interventions, stem cell transplantation, and total knee replacement. The spatial model for Germany involved three steps: (1) delimiting cooperating hospitals, (2) iterative grouping, and (3) categorising hospital groups.

Results: The grouping process described above resulted in 55 (oesophagus), 126 (pancreas), 39 (stem cell), and 672 (knee) groups across Germany. A total of 50.9%, 49.2%, 51.3%, and 81.5% respectively of these groups contained only one hospital (no cooperation needed). 7, 28, 2, and 22 groups require joint MCR compliance, whereas 19, 21, 8, and 8 hospitals are recommended special permission with a reduced caseload threshold to ensure spatial accessibility for certain regions. The results inform regional policy makers based on the hospital decision space.

Conclusions: This study models potential hospital cooperation based on proximity and caseload, introducing joint MCR compliance. The modelling process supports the formation, categorisation, and analysis of hospital groups, with parameter thresholds enabling flexible policy testing. This approach considers hospitals' intentions and motivations regarding MCRs, and reserves their decision space. This spatial model provides a theoretical basis for granting exceptional MCR permissions to improve spatial accessibility.

Background: Social interaction is essential for health and well-being, given the growing public health concern of social isolation and loneliness. The role of the built environment in supporting social interaction has been widely studied. However, previous research has often treated social interaction as a single, undifferentiated category, although different types of interaction may serve distinct social functions and be influenced by different environmental factors. Moreover, most studies have focused primarily on residential neighborhood contexts. This study addresses these key gaps by distinguishing between two types of social interaction-tie formation and tie maintenance-and by examining built environment characteristics across broader, individualized multidimensional activity space models.

Method: Using data from a Public Participatory GIS (PPGIS) survey (n = 386) in Turku, Finland, this study analyzed how residential urban form and built environment features relate to tie formation and tie maintenance. Built environment features were assessed using three activity space models: 500-meter home buffer, combined buffer around home and daily destinations, and individualized activity range spanning between home and destinations. Structural Equation Modeling was used to examine how these factors influence each type of social interaction and associated psychosocial outcomes.

Results: Residing in urban areas was significantly associated with tie maintenance but not with tie formation. Walkability around the home supported both types of interaction, whereas parks and green spaces near daily destinations were positively associated with tie formation. A similar pattern was observed within individualized activity ranges, where park ratio predicted tie formation. These two types of social interaction influenced psychosocial outcomes through distinct pathways: tie formation had direct positive effects on health and well-being, while tie maintenance contributed indirectly through increased relationship satisfaction.

Conclusion: The findings emphasize the importance of distinguishing between different types of social interaction and accounting for their unique spatial and functional drivers. Urban planning and public health efforts should consider how different aspects of the built environment foster both the formation and maintenance of social ties. Promoting environments that support diverse forms of social interaction is essential not only for enhancing health and well-being but also for reducing the risk of loneliness.

Downscaling areal health data to a finer resolution is important for understanding the intricate spatial patterns of disease. It helps to identify shared risk factors and to develop targeted public health interventions. This paper introduces Area-to-Area (ATA) and Area-to-Point (ATP) Poisson cokriging for downscaling spatial disease risks from aggregated areal data. The methodology addresses key challenges by incorporating correlation between the diseases, adjusting for population heterogeneity, and the varying shapes and sizes of the spatial entities. Simulation studies demonstrate the superior performance of ATA and ATP Poisson cokriging compared to their univariate counterparts. We achieved lower mean squared prediction errors and better preserved small-scale spatial variations. The methods are applied to COVID-19 and asthma occurrences in Bogota, Colombia. They reveal more detailed hotspots and coldspots and refined estimates of COVID-19 risk by leveraging its correlation with asthma. Our methods offer advantages in multivariate disease mapping by enabling more accurate risk assessment, improved small-area estimation, and enhanced understanding of spatial disease patterns. Their ability to downscale risks for multiple diseases simultaneously provides valuable insights for targeted public health interventions and resource allocation.

Background: Identifying spatial patterns or anomalies in medical care related administrative data is a valuable asset to plan the care system. However, the applicability of any results depends on the statistical robustness and spatial resolution of the analysis.

Methods: This study proposes a new method for designing the spatial units for a country-wide analysis, based on the iterative merging of the postal code areas. The method aims to find a trade-off between fine spatial resolution and districts with a statistically relevant number of episodes, also considering the homogeneity of the districts. The method is applied for the spatial analysis of the cardiological rehabilitation care system in Hungary over an 8-year-long period, with the cardiological rehabilitation rate (RR) after acute cardiac events as the dependent variable. We consider two cardiological episode types and perform two separate analyses throughout the study. A voting scheme is used to define the de facto service areas of the dominant providers. Homogeneous spatial clusters with high and low RR values are compared to the boundaries of the service areas using spatial correlation.

Results: The proposed merging method can provide a significantly finer resolution than a simple spatial approach, and the border zones become thinner and clearer between contiguous de facto dominant providers. The spatial analysis found strong clustering with a global Moran I index of 0.80 and 0.85, respectively, and very large regional differences, especially in rural areas of the country, which is consistent with inequities in access or referral pathways. The boundaries of rehabilitation rate anomalies generally match with the dominant service areas of the dominant providers, suggesting that the differences are linked with the anomalies in the professional practice of the providers.

Conclusions: The proposed method proved a useful tool for the spatial analysis of the cardiological rehabilitation network. The method is not specific to the local culture, and it is directly applicable in any other healthcare domain with several service providers and for which population-level, geographically referenced data is available. More research using more elaborate data sources would be needed to understand the root causes of the anomalies detected in the study.

Trial registration: Retrospectively registered.

Background: Chronic kidney disease (CKD) had become one of the increasingly serious public health problems in the world. This study aimed to explore the temporal-spatial distribution characteristics and associated socioeconomic factors of medical expenditures for rural patients with CKD in Fujian province from 2007 to 2016.

Methods: The medical expenditures information of patients with CKD was abstracted from the database of New Rural Cooperative Medical Scheme. Geographically and temporally weighted regression model was used to analyze the associations between per capita annual medical expenditures and six socioeconomic factors at the county level.

Results: The number of rural patients with CKD who visited in medical institutions increased from 3,099 in 2007 to 19,803 in 2016. The total and per capita medical expenditures of rural patients with CKD increased to 545.4 million yuan and 27,539.7 yuan in 2016, respectively. The ratio of per capita out-of-pocket expenses to per capita disposable income decreased from 108.5% in 2007 to 63.2% in 2016. The top 10% of patients with the highest total medical expenditures account for 31.2% ~ 52.5% of total medical expenditures from 2007 to 2016. The counties with high per capita annual medical expenditures mainly concentrated in the southern region and Longyan city. In which, the per capita annual medical expenditures were negatively associated with the percentage of female patients and number of health technicians per 10,000 persons, and positively associated with the percentage of patients who aged ≥ 60 years, percentage of patients whose length of stay > 10 days, per capita annual disposable income and number of beds per 10,000 persons.

Conclusions: The out-of-pocket ratio of rural patients with CKD decreased, but suffering from CKD was still catastrophic. The distribution of medical expenditures in rural residents was uneven and there was temporal-spatial heterogeneity in the associations between per capita annual medical expenditures and socioeconomic factors. It is necessary to improve the awareness and health literacy of residents, systematically carry out CKD screening program in high-risk populations, incorporate CKD into the National Basic Public Health Service Program and increase the number of health technicians which could effectively delay the disease progression and reduce medical expenses.

The first case of COVID-19 in Burkina Faso was reported in March 2020. As of June 8, 2025, Burkina Faso reported 22,114 confirmed cases and 400 deaths. However, few studies have investigated the spatiotemporal dynamics of pandemics within the national boundaries. This study provides a retrospective spatial analysis of COVID-19 transmission in Burkina Faso and identifies the key geographic drivers. Case statistics from March 2020 to December 2021 were sourced from the Directorate of Health Information Systems of the Ministry of Health. Covariates were identified through a literature review and retrieved from local and online resources. Spatial and temporal patterns were analyzed using ArcGIS Pro® 3.4.3. Hotspots and directional trends were mapped using Getis-Ord Gi* statistics and standard deviation ellipses, and district-level spatial associations were evaluated. Multiscale Geographically Weighted Regression (MGWR) was used to model the relationships between disease incidence and geographic features. Five major transmission phases were observed. Specifically, 20 Health Districts were affected between March and April 2020, 38 in September 2020, 62 in April 2021, and 67 in December 2021. Initially, a single hotspot centered in Ouagadougou was identified. A second hotspot emerged in Bobo Dioulasso in September 2020, considerable heterogeneity in case distribution was noted across the districts. The MGWR results highlight population density, poverty rate, relative wealth index, and distance to testing centers as the main spatial drivers, collectively explaining 70% of the variance in incidence. The findings revealed a fast-evolving outbreak with significant spatial variation, revealed the need for adaptive, geography-informed responses. This multiphase framework can inform real-time risk forecasting and improve epidemic preparednessin in low-resource settings.