International Journal of Health Geographics最新文献

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.

Background: Communicable diseases remain a significant public health challenge in Asia, driven by diverse climatic, socioeconomic, and healthcare-related factors. Despite reductions in diseases such as tuberculosis and malaria, persistent hotspots highlight the need for deeper investigation. This study applies machine learning and spatial analysis techniques to examine patterns and determinants of communicable diseases across 41 countries from 2000 to 2022.

Methods: Data were sourced from global repositories, including WHO, CRU TS, WDI, and UNICEF, covering disease cases (e.g., tuberculosis, dengue, malaria), climaticvariables (e.g., precipitation, humidity), and healthcare metrics (e.g., hospital bed density). Missing values were imputed using random forest methods. Outlier detection was conducted using Mahalanobis distances, identifying and addressing significant deviations to ensure data consistency. Models like XGBoost and Random Forest were assessed using RMSE, MAE, and R². SHAP and XAI frameworks improved interpretability, while Gi* spatial statistics revealed disease hotspots and disparities.

Results: Tuberculosis cases declined from 8.01 million (2000) to 7.54 million (2022), with hotspots in India (Gi* = 3.07) and Nepal (Gi* = 4.67). Malaria cases dropped from 27.00 million (2000) to 7.96 million (2022), yet Bangladesh (Gi* = 4.13) and Pakistan (Gi* = 4.17) exhibited sustained risk. Dengue peaked at 2.71 million cases in 2019, with current hotspots in Malaysia (Gi* = 2.4) and Myanmar (Gi* = 0.79). Spatial disparities underscore the influence of precipitation, relative humidity, and healthcare gaps. XGBoost achieved remarkable accuracy (e.g., tuberculosis: RMSE = 0.94, R² = 0.91), and SHAP analysis revealed critical predictors such as climatic factors.

Conclusion: This study demonstrates the effectiveness of integrating machine learning, spatial analysis, and XAI to uncover disease determinants and guide targeted interventions. The findings offer actionable insights for improving disease surveillance, resource allocation, and public health strategies across Asia.

Background: Suicide remains a major public health concern worldwide, responsible for more than 700,000 deaths in 2021, accounting for approximately 1.1% of all global deaths. While many high-income countries have reported declines in age-standardized suicide rates over the past two decades, recent evidence from Spain indicates increasing mortality among women, whereas suicide rates among men have remained relatively stable. To better understand these patterns and their potential underlying determinants, this study examines the spatial and temporal patterns of age-stratified suicide mortality across Spanish provinces from 2010 to 2022, with particular attention to sex-specific differences.

Methods: Mixed Poisson models were applied to analyze provincial- and temporal-level suicide mortality rates, stratified by age and sex. The models accounted for spatial and temporal confounding effects and examined associations with various socioeconomic and contextual factors, including rurality and unemployment.

Results: Findings highlight the influence of rurality and unemployment on suicide mortality, with distinct gender-specific patterns. A 10% increase in the proportion of residents living in rural areas was associated with more than a 5% rise in male suicide mortality, while a 1% increase in the annual unemployment rate was linked to a 2.4% increase in female suicide mortality. Although male suicide rates remained consistently higher than female rates, a notable and steady upward trend was observed in female suicide mortality over the study period.

Conclusions: The use of sophisticated statistical models permits the detection of underlying patterns, revealing both geographic and temporal disparities in suicide mortality across Spanish provinces.

Background: Streets are important spaces for everyday activities, and the street environment can impact quality of life. This study investigates the impact of streetscape visuals on public emotions in the Beishan Street and Nanshan Road historic districts surrounding West Lake in Hangzhou.

Methods: Emotional perceptions were analyzed through a textual study of relevant posts on Sina Weibo, categorizing them into positive or negative sentiments. Simultaneously, Baidu Map Street View Images (SVIs) of the area were processed using the DeepLabV3 + Network model to semantically segment them into 19 elements, followed by the calculation of seven visual space indicators.

Results: Correlation analysis between these street view elements, visual space indicators, and the emotional content of the posts revealed that multiple factors within the SVIs are significantly associated with public emotions. For instance, elements such as Rider and visual space indicators like Enclosure are positively correlated with emotions, while elements like Traffic Light and visual space indicators such as Openness show negative correlations. These emotional impacts vary depending on the specific building types present in the streetscape.

Conclusions: The findings underscore the association between streetscape visuals and public emotions and demonstrate that social media platforms provide substantial data for studying these effects. This research offers valuable insights for urban planners and managers to understand resident and tourist preferences, providing an objective foundation for enhancing urban streetscape quality.

In rapidly urbanizing megacities, the allocation of healthcare resources has long faced the dual challenges of spatial inequity and insufficient hierarchical diagnosis and treatment systems. This study constructed a multi-scale spatial analysis framework in Nanjing, China, to systematically diagnose the supply-demand mismatch of healthcare resources. By integrating the community-level detailed units and 100-meter population raster data, we combined the Hierarchical Two-Step Floating Catchment Area (H2SFCA) method with empirically calibrated service radii and introduced the "per capita bed compliance rate" to address the contradictions between "statistical adequacy" and "functional efficiency" in high-density clusters. The study revealed three key findings: First, medical resources in Nanjing present a "core-periphery" mismatch structure, tertiary hospitals are over-concentrated in the urban core (HH cluster), while per capita bed availability falls below the threshold (0.8 beds per thousand people), posing a hidden risk of overload. Second, secondary hospitals demonstrate a double paradox (LH-type shortages in old city and HL-type excesses in the suburbs), while the primary facilities fail to serve 32.57% of high-demand communities, contrasting sharply with inefficient HL-type redundancies found in remote suburbs. Additionally, 5% of transitional areas show statistically insignificant supply-demand correlations due to the disconnect between population mobility and static data. Based on these insights, the study proposes a two-path optimization framework-"Targeted interventions by LISA cluster type + hierarchical coordination (via referral networks)"-which offers an actionable pathway toward precision-oriented resource allocation. This approach not only provides practical solutions for establishing a"15-minute medical circle" in Nanjing but also presents a methodological paradigm applicable to high-density cities worldwide seeking effective strategies for hierarchical diagnosis and treatment.

Cardiovascular diseases (CVD) account for 40% of deaths in China, with increasing prevalence associated with rapid urbanization and aging populations. Current research lacks comprehensive analysis of macro-scale environment-socioeconomic interactions. This study establishes a framework analyzing five environmental determinants of CVD disability-adjusted life years (DALYs): air quality (M₁), green space accessibility (M₂), public service facilities (M₃), natural conservation status (M₄), and transportation infrastructure (M₅). Using 2000-2019 national and provincial data, we applied partial least squares structural equation modeling (PLS-SEM) to quantify direct/mediated effects, complemented by spatial heatmaps. Results reveal: (1) urbanization indirectly reduces CVD burden through improved transportation infrastructure (β = - 1.396, p < 0.1); (2) natural reserves provide the strongest protection (β = - 1.235, p < 0.01) with time-lagged effects; (3) significant synergy between green spaces and public services (r = 0.69); (4) high-risk provinces (e.g., Yunnan, Fujian) require geographically tailored strategies. The results can provide evidence-based planning strategies for CVD-mitigating urban development.

Obesity varies markedly across U.S. counties, and global models often miss place-specific determinants. While prior studies document higher prevalence in nonmetropolitan areas, the geographic variation in its determinants-known as spatial heterogeneity-remains underexplored. We linked age-adjusted adult obesity prevalence with socioeconomic indicators, and behavioral risks for 3106 contiguous counties. A global OLS model served as a baseline, followed by estimation of Multiscale Geographically Weighted Regression (MGWR). MGWR outperformed global OLS (adjusted [Formula: see text]: 0.801 vs. 0.566; AICc: 13,580.92 vs. 15,764.21), confirming non-stationarity and revealing covariate-specific scales. Metropolitan status was generally protective, but its effect attenuated or reversed in parts of the West. Income and educational attainment are broadly inverse with minimal dispersion across counties, suggesting near-global behavior in this specification. Short sleep shows a strong positive association with little spread, while binge drinking is positive and slightly more variable. Employment is narrowly positive with almost no spatial dispersion. Bandwidth diagnostics separate near-global from local processes: metro and employment operate at large bandwidths, education and binge drinking at meso scales, and income, short sleep, and marriage at finer scales. As a benchmark, metro-only models showed a uniformly protective but locally varying metro effect that attenuated once socioeconomic and behavioral covariates were included. Findings confirm non-stationarity and argue for a two-tier translation: system-level policies for near-global factors and community-tailored interventions for localized risks, with attention to Western metropolitan vulnerabilities and Southeastern rural constraints.