International Journal of Health Geographics最新文献

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.

Against the backdrop of China's continuously intensifying population aging, the spatially balanced distribution of elderly care institutions (ECIs) has emerged as a critical issue for alleviating elderly care pressure and advancing social equity. Utilizing nationally registered ECI data, this study integrates ArcGIS spatial analysis with an Optimal-Parameter Geographical Detector (OPGD) approach to systematically investigate the spatial heterogeneity, supply-demand imbalance patterns, and underlying formation mechanisms of ECIs in China at the provincial level. A key finding is the pronounced spatial and structural imbalance between supply and demand. Kernel density estimation reveals a multi-level clustering structure centered on Shanghai and Chongqing, while the consistency coefficient identifies distinct mismatch patterns: regions such as Xinjiang and Northeast China experience "supply exceeding demand," whereas economically dynamic areas like the Pearl River Delta face "supply falling behind demand." Spatially, ECIs overall follow a "dense southeast-sparse northwest" pattern closely aligned with the "Hu Huanyong Line," with six provinces including Henan and Sichuan accounting for 34.1% of institutions, compared to only 1.6% in four western provinces/regions and Hainan. Furthermore, OPGD analysis identifies the permanent population size and number of hospital beds as the dominant factors influencing the spatial layout of ECIs. Their interaction with public transportation accessibility and fiscal expenditure significantly enhances explanatory power, highlighting the crucial role of medical-care integration and government investment in resource allocation. This study provides a scientific basis for optimizing the spatial allocation of elderly care resources and promoting coordinated regional development in China.

Background: The COVID-19 pandemic displayed notable disparities in infection and mortality rates across populations, yet socioeconomic factors remain underexplored in many analyses. This study leverages an individual-level dataset from Cali, Colombia, detailing COVID-19 cases, vaccination histories, and mortality outcomes, to examine spatiotemporal vaccination patterns and their effects on mortality.

Methods: Using a Bayesian two-part model with generalized linear mixed models, the analysis accounts for endogenous selection, individual heterogeneity, and spatial-temporal dependencies.

Results: The findings highlight significant socioeconomic inequalities in vaccination coverage: individuals from higher socioeconomic strata were more likely to receive full vaccination regimens and booster doses, while those from lower strata faced reduced vaccination coverage and elevated mortality risks. Employment, socioeconomic status, and ethnicity emerged as key predictors of vaccination propensity and mortality, disproportionately disadvantaging vulnerable groups.

Conclusions: These results stress the need for equitable vaccine distribution and targeted interventions to address disparities and enhance public health outcomes.

Background: Influenza can cause serious complications in individuals with chronic diseases. Although vaccination is strongly recommended for the high-risk population, uptake remains suboptimal. This retrospective cohort study assessed the relationship between demographic, clinical, and socio-economic (SE) factors and influenza vaccination uptake among high-risk patients in the Apulia region over four influenza seasons (2019-2023).

Methods: Data on comorbidities, vaccination history, and demographics were extracted from the User Fee Exemption Registry, the Immunization Information System, and the Total Population Register, respectively. Each geocoded case was linked to the Italian National Deprivation Index to determine SE status at the census tract level. Descriptive statistics, logistic regression, and multilevel mixed general linear models were used to analyze factors associated with vaccination uptake.

Results: Vaccination coverage among people with longstanding illnesses was 35.5% in 2019-2020, peaked at 44.7% in 2020-2021, and declined thereafter (42.9% in 2021 - 2022; 40.1% in 2022 - 2023). Higher uptake was associated with female sex, older age, and a greater number of comorbidities. SE deprivation was inversely associated with vaccination uptake. Individuals with chronic renal/adrenal insufficiency, cardiovascular, or neoplastic diseases had the highest uptake. The data also suggest a potential link between marital status and the likelihood of vaccination.

Conclusions: Demographic, SE, and clinical factors may play a significant role in influenza vaccination uptake. Public health strategies should consider these determinants to improve coverage and reduce health inequalities.

Background: Tuberculosis (TB) is a major global health problem, and the pathogenesis of TB is determined by multiple variables. The complicated relationship between geographic determinants and incidence rates is poorly understood, and multicollinearity and spatial heterogeneity were not considered when exploring this relationship.

Methods: In this study, the factors influencing the incidence of TB in China were investigated, considering spatial heterogeneity, to develop a multidimensional TB indicator system that incorporates geographic factors. A comprehensive linear-nonlinear two-stage feature screening model was developed to identify key factors contributing to TB. The ordinary least squares model was constructed at the national scale using these key indicators to understand the macro-relationships between TB incidence rates and key indicators. A geographically weighted regression (GWR) model was constructed at a provincial scale, and a multiscale geographically weighted regression (MGWR) model was developed to conduct an in-depth comparative analysis of the fitting effects of the GWR and MGWR models on the TB incidence rates. The goal of this study is to investigate the impact of the GWR and MGWR models on TB incidence. The adjustable bandwidth mechanism of the MGWR model was compared with the fixed bandwidth mechanism of the GWR model to determine the best model for geographical analysis of TB incidence.

Results: The MGWR model had the best fit (R² = 0.942; AICc = 57.060) for TB incidence and provided unique bandwidths for important variables to improve model geographic analysis. The analysis of geographic components using the MGWR model revealed that the fitting coefficients of mean height, topographic relief, and average annual precipitation were spatially heterogeneous.

Conclusion: These results provide the theoretical foundation for developing TB prevention and control measures.