International Journal of Health Geographics最新文献

Green spaces provide diverse health benefits, and provision of green spaces is often linked to lower incidences of adiposity. Undergraduates, who are at a transitional stage of development, represent a critical population for obesity prevention. However, recent studies suggest that the health effects of green space may vary by type. Furthermore, inferring any causal relationship between green spaces and adiposity using a cross-sectional research design remains challenging. To address these issues, this study utilized a large, representative sample of 21,990 undergraduates from 89 universities across 29 provinces in China, and employed a quasi-experimental approach to explore the impacts of specific green space types on body mass index (BMI). Propensity score matching was used to make the students who were influenced by green spaces comparable to those who were not. A difference-in-differences model was applied to estimate the causal effects of three types of green spaces (trees, bushes, and grass) on BMI. To further explore the underlying mechanisms, we examined two potential mediators: energy expenditure (physical activity) and energy intake (unhealthy food consumption). The results revealed that trees had a negative impact on BMI, whereas bushes and grass had no significant effect. Physical activity serves as a significant mediator linking tree exposure to adiposity changes, while unhealthy food intake showed no statistically significant mediation effect. In the stratified analysis, trees had significantly negative effects only on males. These findings highlight the importance of distinguishing green space types and provide causal evidence linking tree exposure to reduced BMI among undergraduates.

This study developed and validated the Spatial Methodology Appraisal of Research Tool (SMART) using group concept mapping with discipline experts. The 16-item tool comprises four domains: (1) methods preliminaries, (2) data quality, (3) spatial data problems, and (4) spatial analysis methods. Validity testing demonstrated excellent content validity and expert agreement. Future studies will assess its usability and reliability to ensure consistent results. Its application in spatial epidemiology and health geography will enable more rigorous and transparent evidence synthesis. This contribution represents a significant step forward in improving the standards of quality appraisal in spatial research.

Background: The increasing availability globally of building footprint datasets has brought new opportunities to support a geographic approach to health programme planning. This is particularly acute in settings with high disease burdens but limited geospatial data available to support targeted planning. The comparability of building footprint datasets has recently started to be explored, but the impact of utilising a particular dataset in analyses to support decision making for health programme planning has not been studied. In this study, we quantify the impact of utilising four different building footprint datasets in analyses to support health programme planning, with an example of malaria vector control initiatives in Zambia.

Methods: Using the example of planning indoor residual spraying (IRS) campaigns in Zambia, we identify priority locations for deployment of this intervention based on criteria related to the area, proximity and counts of building footprints per settlement. We apply the same criteria to four different building footprint datasets and quantify the count and geographic variability in the priority settlements that are identified.

Results: We show that nationally the count of potential priority settlements for IRS varies by over 230% with different building footprint datasets, considering a minimum threshold of 25 sprayable buildings per settlement. Differences are most pronounced for rural settlements, indicating that the choice of dataset may bias the selection to include or exclude settlements, and consequently population groups, in some areas.

Conclusions: The results of this study show that the choice of building footprint dataset can have a considerable impact on the potential settlements identified for IRS, in terms of (i) their location and count, and (ii) the count of building footprints within priority settlements. The choice of dataset potentially has substantial implications for campaign planning, implementation and coverage assessment. Given the magnitude of the differences observed, further work should more broadly assess the sensitivity of health programme planning metrics to different building footprint datasets, and across a range of geographic contexts and health campaign types.

Background: Geographic masking is an important but under-utilized technique for protecting and disseminating sensitive geospatial health data. Geographic masks work by displacing static point locations such that the people those locations describe cannot be identified, while at the same time preserving important spatial patterns for analysis. Unfortunately, there is a lack of available tooling surrounding geographic masks which we believe creates an unnecessary barrier towards the adoption of these techniques. As such, this article presents a set of tools for performing, evaluating, and developing geographic masks, called MaskMyPy.

Results: MaskMyPy is an open-source Python package that includes functions for performing geographic masks, including donut, street, location swapping, and Voronoi masks. It also includes a range of tools for evaluating the results of these masks, both with regard to privacy and information loss. Finally, it includes a special class called the 'Atlas' that aims to dramatically streamline mask execution and evaluation. We conducted a short case study to illustrate the power of MaskMyPy in geographic masking research, and in doing so showed that mask performance can range widely due solely to randomization. As such, we recommend that masking researchers test their masks repeatedly across a variety of test datasets.

Conclusion: MaskMyPy makes it easy to apply a variety of geographic masks to a set of sensitive points and then measure which mask provided the most privacy while suffering the least information loss. We believe this style of tooling is important to not only make geographic masks accessible to non-experts, but to enable expert users to better interrogate the masks they develop, and in doing so drive the geographic masking discipline forward.

Background: Accessibility to community pharmacies is crucial for ensuring timely access to medications and essential health services. While accessibility to community pharmacies is critical, disparities driven by temporal and spatial factors persist, resulting in inequities in healthcare access. This study aims to comprehensively assess spatiotemporal and multimodal accessibility to community pharmacies in Lisbon, highlighting the influence of transport modes and time of day on accessibility disparities.

Data and methods: The study employed a methodology that considered five daily time slots and two modes of transport-walking and public transport-to evaluate accessibility to community pharmacies. Data was sourced from road and pedestrian networks, Google API, and GTFS data. Descriptive statistics and spatial analysis were utilized to assess travel time and accessibility disparities across different regions of Lisbon. The analysis focused on both the percentage of residents able to access pharmacies within 10 min and the total number of pharmacies accessible.

Results: ndings reveal significant temporal variations in accessibility, with public transport consistently improving access compared to walking. Accessibility peaks in the evening (6-7 PM), when 83.3% of residential buildings are within a 10-min walking distance of a pharmacy, and 92.7% are reachable by public transport. In contrast, early morning hours (4-5 AM) show the lowest accessibility, with only 8.9% of buildings accessible by walking and 16.1% by public transport. During the daytime (8-9 AM), notable disparities emerge across the city: public transport enhances access in the southwest, northwest, and central areas, while limited pharmacy opening hours constrain accessibility in the north and southeast, where only 108 of 258 pharmacies are operational. Finally, travel time to pharmacy services for city residents highlight significant spatial and temporal disparities in pharmacy accessibility, emphasizing the role of transport modes and service hours in shaping urban healthcare access.

Conclusions: This study underscores the importance of addressing both temporal and spatial factors to ensure equitable accessibility to community pharmacies. The findings suggest the need for targeted policies to improve public transport services during off-peak hours and to extend pharmacy operating hours. Future research should focus on comparative studies across different urban contexts and incorporate more granular data to better understand accessibility to urban services.

Background: Illicit kidney trade networks, operating globally, involve intricate interactions among various players, most notably buyers, sellers, brokers, and surgeons. A comprehensive understanding of these trade networks is, however, hindered by the lack of systematically amassed data for analysis. Further, extracting the geographic locations of buyers, sellers, brokers, transplant surgeons, and medical facilities in all relevant publications often involves extensive, time-consuming, manual labelling that is very costly. Although current techniques such as Named Entity Recognition (NER) tools can potentially automate the process, they are limited to identifying country names and often fail to associate the roles (i.e., offering buyer, seller, broker and/or surgery) that each country played.

Methods: This study employed state-of-the-art technologies, including Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformers (GPT) model Llama3.3 from Meta in developing a kidney trade country database. We first extracted news articles reporting illicit kidney trade from the LexisNexis database (2000-2022). BERT and Llama3.3 with chain-of-thought prompt tuning strategies were then applied to the materials to determine the relevance of articles to the illegal kidney trade and to identify the roles those different countries played in kidney trade cases over the past 23 years. The specific country classes recorded in the final kidney trade database included: a) countries of origin for kidney sellers; b) countries of origin of kidney buyers; c) countries performing illegal transplant surgeries; and d) countries of origin of organ trafficking brokers.

Results: The BERT classification model achieved an accuracy of 88.75%, ensuring that only relevant articles were analyzed. Additionally, the Llama3.3-70B model with chain-of-thought prompt tuning strategies extracted location-based roles with an accuracy of 86.30% for sellers, 88.89% for buyers, 93.33% for brokers, and 95.93% for surgeries, supporting these observed patterns. We observed in the final database that the kidney trade networks change and evolve dynamically where the primary role played by each country (as a host of either sellers, buyers or surgeries) change over time. About half of the top 10 countries playing each role gets replaced by other countries within a decade. The final database also demonstrated that developing countries were more likely to be a host of kidney sellers while that played by developed countries was a host of kidney buyers.

Conclusion: The current study developed a geospatial database describing transnational kidney trade country networks over the past two decades. The new approach for geographic location extraction that is more precise compared to conventional NER and machine learning methods.

Background: Adolescents living in agricultural areas are at higher risk of secondary pesticide exposure; however, there is limited evidence to confirm exposure by pesticide drift for greenhouse floriculture, like rose production.

Methods: 525 adolescents (12-17, 49% male) living in Pedro Moncayo, Ecuador were assessed in 2016. Urinary concentrations of creatinine and pesticide biomarkers (organophosphates, neonicotinoids, and pyrethroids) were measured using mass-spectrometry. Home distance to the nearest greenhouse and surface area of greenhouses within various buffer sizes around the home were calculated. Linear regression assessed whether home distance and surface area of greenhouses was associated with creatinine-adjusted metabolite concentration, adjusting for demographic, socioeconomic, and anthropometric variables. Geospatially weighted regression (GWR) was conducted, adjusting for similar covariates. Getis-ord Gi* identified hot and cold spots using a 1994 m distance band.

Results: The associations between residential distance to greenhouses and urinary pesticide metabolites differed by metabolite type. The adjusted mean concentrations of OHIM (neonicotinoid) were greater (p-difference = 0.02) among participants living within 200 m (1.08 ug/g of creatinine) vs > 200 m (0.64 ug/g); however, the opposite was observed for 3,5,6-Trichloro-2-pyridinol (TCPy, organophosphate; 0-200 m: 3.63 ug/g vs > 200 m: 4.30 ug/g, p-diff = 0.05). In linear models, greater distances were negatively associated with para-nitrophenol (PNP, organophosphate; percent difference per 50% greater distance [95% CI]: - 2.5% [- 4.9%, - 0.1%]) and somewhat with 2-isopropyl-4-methyl-6-hydroxypyrimidine (IMPy, organophosphate; - 4.0% [- 8.3%, 0.4%]), among participants living within 200 m of greenhouses. Concurring with the adjusted means analyses, opposite (positive) associations were observed for TCPy (2.1% [95%CI 0.3%, 3.9%]). Organophosphate and pyrethroid hotspots were found in parishes with greater greenhouse density, whereas neonicotinoid hot spots were in parishes with the lowest greenhouse density.

Conclusion: We observed negative associations between residential distance to greenhouses with OHIM, PNP and to some extent IMPy, suggesting that imidacloprid, parathion and diazinon are drifting from floricultural greenhouses and reaching children living within 200 m. Positive TCPy associations suggest greenhouses weren't the chlorpyrifos source during this study period, which implies that non-floricultural open-air agriculture (e.g. corn, potatoes, strawberries, grains) may be a source. Further research incorporating diverse geospatial constructs of pesticide sources, pesticide use reports (if available), participant location tracking, and repeated metabolite measurements is recommended.

Background: Emissions from wildfire plumes are composed of modified biomass combustion by-products, including carcinogens. However, studies of the association between wildland fires (WF; includes wildfires, prescribed burns, and resource management fires) exposure and lung cancer are scant. We evaluated geographic patterns in these exposures and their association with lung cancer mortality (LCM) rates across the conterminous United States (US).

Methods: We extracted data from the Monitoring Trends in Burn Severity program (1997-2003) and derived county-level exposure metrics: WF density by area, WF density by population, the ratio between total burned land area and county area, and the ratio between total burned land area by population. We obtained sex-specific, county-level LCM rates for 2016-2020 from the National Center for Health Statistics. Counties with fewer than 10 cases were suppressed. To account for cigarette smoking, we first modeled residual values from a Poisson regression between cigarette smoking prevalence and sex-specific, age-adjusted LCM rates. We then used Lee's L statistic for bivariate spatial association to identify counties with statistically significant (p < 0.05) associations between WF exposures and these residuals. In a sensitivity analysis, we applied a false discovery rate correction to adjust for multiple comparisons.

Results: We observed geographic variation in bivariate associations between large WFs and subsequent LCM rates across US counties while accounting for ever cigarette smoking prevalence. There were positive (high WF exposures and high LCM rate) clusters for males and females in counties within the mid-Appalachian region and Florida, and modest differences across WF metrics in the cluster patterns were observed across the Western US and Central regions. The most positive clusters were seen between WF density by area and LCM rates among women (n = 82 counties) and a similar geographic pattern among men (n = 75 counties). Similar patterns were observed for males and females in the western US, with clusters of high WF exposures and low LCM rates. After adjusting for multiple comparisons, a positive cluster pattern among both sexes persisted in Kentucky and Florida with area-based exposure metrics.

Discussion: Our analysis identified counties outside the western US with wildfires associated with lung cancer mortality. Studies with individual-level exposure-response assessments are needed to evaluate this relationship further.

In an era of rapid technological advancements, generative artificial intelligence and foundation models are reshaping industries and offering new advanced solutions in a wide range of scientific areas, particularly in public and environmental health. However, foundation models have previously mostly focused on understanding and generating text, while geospatial features, interrelations, flows and correlations have been neglected. Thus, this paper outlines the importance of research into Geospatial Foundation Models, which have the potential to revolutionise digital health surveillance and public health. We examine the latest advances, opportunities, challenges, and ethical considerations of geospatial foundation models for research and applications in digital health. We focus on the specific challenges of integrating geospatial context with foundation models and lay out the future potential for multimodal geospatial foundation models for a variety of research avenues in digital health surveillance and health assessment.

Generative AI is rapidly establishing itself as a key member of the GeoAI battery of methods, models and tools in use today in various health applications. This paper is the first in an Int J Health Geogr two-article series (2025) on the 'Generative Revolution'. It is meant to serve as a brief introduction to the second article entitled 'The Generative Revolution: AI Foundation Models in Geospatial Health-Applications, Challenges and Future Research'.