International Journal of Health Geographics最新文献

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.

Background: Epidemiological studies investigating long-term health effects of air pollution typically only consider the residential locations of the participants, thereby ignoring the space-time activity patterns that likely influence total exposure. This paper, part of a study in which residential-only and mobility-integrated exposures were compared in two tracking campaigns, reflects on GPS device choice, privacy, and recruitment strategy.

Methods: Tracking campaigns were conducted in Switzerland and the Netherlands. Participants completed a baseline questionnaire, carried a GPS device (SODAQ) for 2 weeks, and used a smartphone app for a time activity diary. The app also tracked GPS, albeit less frequently. Tracks were combined with air pollution surfaces to quantify NO₂ and PM_2.5 exposure by activity.

Results: In Switzerland, participants were recruited from the COVCO-Basel cohort (33% recruitment rate; 489 of 1,475). In the Netherlands, -random recruitment was unsuccessful (1.4% rate; 41 of 3,000). Targeted recruitment with leaflets and a financial incentive (25 Euro voucher) increased participation to 189. Comparisons between smartphone app and SODAQ device data showed moderate to high correlations (R2 > 0.57) for total NO₂ exposure and NO₂ exposure at home in both study areas. Activity-specific correlations ranged from 0.43 to 0.63. PM_2.5 correlations in Switzerland were moderate to high, but lower in the Netherlands (R² = 0.28-0.58), due to smaller spatial contrast in observed PM_2.5 levels (RMSE < 0.68 µg/m³).

Conclusions: Tracking can be effectively conducted using a mobile app or GPS device. The app's low-frequency GPS readings (every 3-4 min) were sufficient for long-term air pollution exposure assessment. For finer-scale readings, a dedicated GPS device is recommended. Tracking campaigns are crucial for studying personal exposure to air pollution but face challenges due to low recruitment rates and strict privacy regulations. Leveraging an existing cohort can improve recruitment, while targeted leaflet distribution with financial incentives can enhance participation in studies without a pre-recruited group.

Background: As urbanization accelerates, the height of urban buildings continues to rise, which may influence the provision of Emergency Medical Services (EMS). However, a current limitation is that related studies often neglect the impact of spatial variations in building height on EMS accessibility equality. Most scholars have focused primarily on EMS road travel-either the Departure Road Trip (DRT) or the Transport Trip (TT)-while overlooking the effects of building height on the in-building EMS trip, known as the Patient Access Trip (PAT).

Methods: EMS accessibility was measured using a proximity-based method and a Gaussian two-step floating catchment area (G-2SFCA) model under two scenarios: Scenario 1 considered only DRT, whereas Scenario 2 incorporated both DRT and PAT influenced by building heights. DRT travel times were simulated using Baidu Map's Application Programming Interface (API), and PAT times were calculated based on building elevator/stairs characteristics. Accessibility equality was assessed using multi-ring buffer analysis, Lorenz curves, and Gini coefficients.

Results: According to the empirical study in Wuhan, China, first, the spatial variations in building height was evident across the city. The building heights in city centre and sub-centres are generally taller compared to those in suburban areas. Second, the variations in building height can obviously affect EMS accessibility. However, the impact of building height on EMS accessibility varies across different regions. The effect is particularly pronounced in sub-centres located around 14 km from the city centre, whereas it is relatively limited in suburban areas. Third, the incorporation of spatial disparities in building height into EMS accessibility modeling reveals increased inequality in EMS provision across the city.

Conclusion: Spatial disparities in building heights across a city significantly influence EMS accessibility inequality. Given the widespread differences in building heights worldwide, this study provides valuable findings for healthcare policymakers to improve EMS systems.