International Journal of Health Geographics最新文献

Gun violence is a leading cause of death in the United States. Understanding the geospatial patterns of gun violence and how the COVID-19 pandemic may have affected them is essential for developing evidence-based prevention strategies. This study investigates whether COVID-19 altered the geospatial patterns of gun violence in Syracuse, New York. To assess spatial and temporal trends, we analyzed the annual total gunshots (ATG) from 2009-2023 aggregated in census block groups and applied geospatial techniques including mean center, standard distance, Moran's I, and Getis-Ord Gi*. The ATG number was higher before the pandemic than during the pandemic, something not observed in other studies. Its geographic centers before and during the pandemic clustered within or near one census block and the associated standard distance remained similar between the two periods. Both global patterns and local clusters of ATG in the two periods not only showed similar patterns and consistent local hotspots located in similar areas, but also logarithmically related to the ATG number with statistical significance, suggesting that gun violence rates intensified within established areas rather than spreading citywide and demonstrated a similar distance-decay effect in both periods. This effect suggests that the incidence of gunshots diminished with increasing distance from the core concentrated zone, challenging assumptions of spatial spillover or contagion models in crime studies. These findings suggest that entrenched structural conditions, such as neighborhood-level socioeconomic disparities, are the primary drivers of gun violence patterns, rather than temporary pandemic-related policies. Methodologically, the study highlights the importance of long-term, meso-scale geospatial analyses to uncover persistent violence dynamics and guide preventive interventions. We argue that future violence prevention strategies should focus on enduring geospatial patterns of gun violence and their underlying structural determinants, rather than reacting solely to short-term fluctuations in incident frequency.

Background: Mental disorders significantly burden Indigenous communities, worsened by limited culturally appropriate services. Spatial inequalities in access further disadvantage Indigenous peoples, especially in socio-economically challenged areas. This paper measures the spatial accessibility of Indigenous community-controlled mental health services in South East Queensland, Australia and examines its social inequalities across the region.

Methods: We considered both population and health service providers' capacity to maximise service coverage in measuring potential access to the services. Using Geographical Information Systems (GIS) technologies, a Gaussian-based two-step floating catchment area (G2SFCA) method was applied to quantify accessibility under four driving time thresholds ranging from 15 to 60 minutes. Bivariate global and local Moran's I statistics were used to analyse social inequalities in accessibility across various geographical areas.

Results: Accessibility was higher in urban areas than those towards the peri-urban and rural areas; the overall spatial coverage was relatively limited for service access within the 15- or 30-minute driving time threshold, compared with the 45- or 60-minute driving time threshold. Lower levels of accessibility were identified in areas with a concentration of Indigenous and socio-economically disadvantaged populations.

Conclusions: This study advances a socially informed spatial inequality assessment framework. Unlike previous research exploring accessibility qualitatively, our framework innovatively integrates spatial analysis, Indigenous-specific population data and culturally sensitive provider capacity metrics within an advanced G2SFCA model. This approach uniquely exposes the compounded socio-spatial barriers to mental health services for Indigenous populations across South East Queensland's urban-rural continuum. The resulting accessibility and inequality maps, combined with a summary of focus areas and their associated socio-demographic profiles, provide a direct policy lever to prioritise intervention for Indigenous communities experiencing the greatest disadvantage. By bridging spatial analysis with Indigenous cultural contexts, this work offers a replicable model for equitable, community-driven healthcare resource allocation for Indigenous peoples globally.

Background: While road traffic noise is an emerging environmental risk for cardiovascular mortality, its age-group-specific effects on stroke mortality remain unclear. This study further explored socioeconomic disparities in this association.

Methods: We conducted a retrospective cohort study (2011-2019) with 36,240 hospitalized stroke patients in Fuxin, China. Residential noise levels were estimated using street view imagery analyzed by a novel and multimodal deep learning model. Age-grouped cox proportional hazards models adjusted for NO₂, NDVI (Normalized Difference Vegetation Index), and sociodemographic covariates were applied to assess mortality risks.

Results: Among elderly patients aged ≥60 years with lower medical insurance, each 5-dB increase in residential road noise was associated with a 93.6% increase in stroke mortality risk (HR = 1.936, 95% CI: 1.024-3.660; p = 0.042). The estimated exposure prevalence in this subgroup was 3%, yet the population attributable fraction reached 1.7%. In contrast, no significant associations were found among patients with higher insurance coverage. Younger Males had a 51.3% higher mortality risk than females (adjusted HR=1.513, 95% CI: 1.142-2.005), independent of environmental exposures. NO₂ and NDVI were not significantly associated with mortality across subgroups.

Conclusions: These findings highlight the need for noise mitigation strategies that prioritize vulnerable populations, particularly the elderly and those with limited healthcare access.

Background: To address challenges arising from rapid urban development, China has formulated and implemented the New-Type Urbanization strategy. However, empirical research on the specific impacts between New-Type Urbanization and health expenditures remains limited.

Methods: Using panel data from 31 Chinese provinces (2012-2019), this study constructed a comprehensive evaluation index system for New-Type Urbanization across four dimensions: demographic, economic, social, and ecological. Geographically and Temporally Weighted Regression was employed to examine the spatial effects, influencing factors, and spatial heterogeneity of New-Type Urbanization's impact on health expenditures.

Results: The results show that China's health expenditures primarily exhibit High-High and Low-Low clustering patterns with spatial fluctuations. Meanwhile, the impact of New-Type Urbanization on health expenditures demonstrates spatiotemporal heterogeneity and non-stationarity. As urbanization levels increase, the negative effects of health expenditure clustering expand, while the influence of economic urbanization weaken.

Conclusions: Our findings fill the research gap regarding the impacts between New-Type Urbanization and health expenditures, while also providing direction for New-Type Urbanization development to support the implementation of health policies aimed at controlling health expenditure growth.

Background: The optimal placement of ambulances is critical for ensuring timely emergency medical responses, especially in regions with high accident frequencies. In Rwanda, where road accidents are a leading cause of injury and death, the strategic positioning of ambulances can significantly reduce response times and improve survival rates. The national records of Rwanda reveal a rising trend in the number of road accidents and deaths. In 2020, there were 4203 road traffic crashes throughout Rwanda with 687 deaths, data from 2021 demonstrated 8639 road traffic crashes with 655 deaths. Then in 2022 national statistics indicated 10,334 crushes with 729 deaths. The study used emergency response and road accident data collected by Rwanda Biomedical Centre in two fiscal years 2021-2022 and 2022-2023 consolidated with the administrative boundary of Rwandan sectors (shapefiles).

Methods: The main objective was to optimize ambulance locations based on road accident data using machine learning algorithms. The methodology of this study used the random forest model to predict emergency response time and k-means clustering combined with linear programming to identify optimal hotspots for ambulance locations in Rwanda.

Results: Random forest yields an accuracy of 94.3%, and positively classified emergency response time as 926 fast and 908 slow. K-means clustering combined with an optimization technique has grouped accident locations into two clusters and identified 58 optimal hotspots (stations) for ambulance locations in different regions of Rwanda with an average distance of 1092.773 m of ambulance station to the nearest accident location.

Conclusion: Machine learning may identify hidden information that standard statistical approaches cannot, the developed model for random forest and k-means clustering combined with linear programming reveals a strong performance for optimizing ambulance location using road accident data.

Background: Malaria continues to be one of the most significant infectious diseases in terms of morbidity and mortality. In many parts of North America, including parts of southern Canada, competent malaria vectors Anopheles quadrimaculatus and Anopheles freeborni are present. With climate change, Canada may be increasingly suitable for transmission of the malaria parasite Plasmodium spp. The objective of this study was to identify the geographic locations in Canada where, and the frequency with which, temperature conditions may be suitable for autochthonous transmission of Plasmodium vivax and Plasmodium falciparum under current and projected climate.

Methods: Temperature and duration thresholds from historic Plasmodium spp. transmission studies were applied on gridded historical and projected data to compute yearly frequencies of suitable conditions in Canada.

Results: The resulting yearly frequencies from 2000 to 2023 show rising trends for both Plasmodium species, with surges reaching 34% of the Canadian population temporarily living under suitable temperature conditions for Plasmodium falciparum, and 56% for Plasmodium vivax. Projected populations percentages vary significantly with the Plasmodium species, climate change scenario, and climate model considered.

Conclusion: Our results underscore the increasing risk of autochthonous transmission of malaria in Canada due to climate change.

Background: Dengue fever is transmitted to humans through bites of Aedes mosquito vectors. Therefore, controlling the Aedes population can decrease the incidence and block transmission of dengue fever and other diseases transmitted by these mosquito species. In many countries, gravitraps are used to monitor mosquito vector densities, but this approach usually underestimates the population of Aedes mosquitoes. Moreover, literature on the spatio-temporal dynamics of Aedes populations in a single city is limited. For example, in Kaohsiung of Taiwan, population densities vary substantially between villages, and the city government has relatively limited resources to deploy gravitraps. Therefore, a well-defined index should be developed to reflect the spatial-temporal dynamics of adult Aedes mosquitoes in urban environments. This would allow reduction of sources and removal of vector habitats under various situations.

Methods: An artificial intelligence (AI) surveillance based on an auto-Markov model with a non-parametric permutation test is proposed. The auto-Markov model takes neighborhood effects into consideration, and can therefore adjust spatial-temporal risks dynamically in various seasons and environmental background. Information from neighboring villages is incorporated into the model to enhance precision of risk prediction.

Results: The proposed AI gravitrap index integrates the auto-Markov and disease mapping models to enhance sensitivity in risk prediction for Aedes densities. Simulation studies and cross-validation analysis indicated that the AI index could be more efficient than traditional indices in assessing risk levels. This means that using the AI index could also reduce allocation cost for gravitraps. Moreover, since the auto-Markov model accommodates spatial-temporal dependence, a risk map by the AI index could reflect spatial-temporal dynamics for Aedes densities more accurate.

Conclusions: The AI gravitrap index can dynamically update risk levels by the auto-Markov model with an unsupervised permutation test. The proposed index thus has flexibility to apply in various cities with different environmental background and weather conditions. Furthermore, a risk map by the AI index could provide guidance for policymakers to prevent dengue epidemics.

Background: Clonorchis sinensis, the liver fluke responsible for clonorchiosis, presents a persistent public health burden in Guangxi (Southern China) and Vietnam. Its transmission is influenced by a complex interplay of ecological, climatic, and socio-cultural factors.

Methods: We compiled infection occurrence data from systematic literature reviews and national surveys conducted between 2000 and 2018. Environmental and climatic predictors were obtained from long-term raster datasets. Machine learning models, including logistic regression and tree-based ensemble methods, were used to assess associations between predictor variables and C. sinensis presence. Partial dependence plots were employed to refine predictor selection and explore marginal effects.

Results: Raw freshwater fish consumption was identified as the most influential predictor. In Guangxi, 54.9% of counties reported raw fish consumption, compared to 31.7% in Vietnam. Logistic regression achieved the highest predictive accuracy (AUC = 0.941). Climatic comparisons showed that Vietnam had a higher annual mean temperature (Bio1: 23.37 °C vs. 20.86 °C), greater temperature seasonality (Bio4: 609.33 vs. 464.92), and higher annual precipitation (Bio12: 1731.64 mm vs. 1607.56 mm) than Guangxi, contributing to spatial differences in endemicity. High-risk zones were concentrated along the China-Vietnam border, suggesting the need for geographically targeted interventions.

Conclusion: The findings underscore the combined influence of ecological and behavioral factors on C. sinensis transmission. The predictive modeling framework offers valuable insights for surveillance planning and cross-border disease control, reinforcing the role of ecological epidemiology in guiding parasitic disease prevention strategies.