Chao Song
Xiuli Wang
Erjia Ge4
Xun Shi
Jay Pan- 1HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- 2West China-PUMC C.C. Chen Institute of Health, Sichuan University, Chengdu, China
- 3Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, China
- 4Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- 5Department of Geography, Dartmouth College, Hanover, NH, United States
- 6China Center for South Asian Studies, Sichuan University, Chengdu, China
Editorial on the Research Topic
Applications of geospatial information technologies and spatial statistics in health services research
The integration of geospatial information technologies and spatial statistical methodologies into health services research constitutes a paradigm shift from conventional non-spatial analyses that have historically overlooked the pivotal laws of geography (1). Over the last decade, a remarkable expansion has occurred within the realm of health economics and public health, specifically in the adoption of advanced geospatial technologies and sophisticated spatial health statistical techniques (2). This editorial seeks to highlight and critically appraise some of the cutting-edge applications of these technologies and methods, elucidating their significance in addressing contemporary public health challenges, particularly within health services research (3).
Within the scope of health services research, two critical dimensions—spatial equity and spatial accessibility—have garnered significant attention (4). This Research Topic features four pivotal articles that delve into these dimensions. In the study “Measuring the inequalities in healthcare resource in facility and workforce: a longitudinal study in China,” Dong et al. investigate the nuanced aspects of spatial inequality in healthcare resource distribution, addressing both the propensity toward equalization and the tendency for geographic agglomeration. In light of their work, we advocate for a shift in focus toward fine-grained, small-area analyses that are simultaneously precise and holistic, advocating for multidimensional assessments that consider driving factors, spatiotemporal dynamics, and the integrative evaluation of various indicators (5, 6).
On the matter of spatial healthcare accessibility, Hua et al., in “Are the epidemic prevention facilities effective? How cities should choose epidemic prevention facilities: taking Wuhan as an example,” utilize travel time as a core measure of accessibility for essential and emergency epidemic prevention facilities. Notably, the Two-Step Floating Catchment Area (2SFCA) method, originally conceptualized by Wang, stands as the benchmark for evaluating spatial accessibility within the healthcare sector (7). Building upon this, Wang et al., in “Disparities in spatial accessibility of primary care in Louisiana: from physical to virtual accessibility,” introduce the novel Two-Step Virtual Catchment Area (2SVCA) method. This innovative approach assesses the spatial accessibility of primary care services through telehealth, thus expanding the concept to embrace virtual accessibility. Furthermore, Molenaar et al., in “Travel scenario workshops for geographical accessibility modeling of health services: a transdisciplinary evaluation study,” establish an essential premise that enhancing and standardizing knowledge elicitation processes for the creation of realistic travel scenarios, encompassing transportation modes and velocities, is crucial for accurately calculating geographical access to health services.
The trifecta of geo-environmental big data, Geographic Information Science (GIS), and spatial statistics stands at the forefront of contemporary health research, paving the way for groundbreaking insights and advancements. The three remaining articles in this Research Topic encapsulate the innovative application of these tools in elucidating complex health-related issues.
In “Comparison of the association between different ozone indicators and daily respiratory hospitalization in Guangzhou, China,” Lin et al. utilize a spatial interpolation method to transform point-specific environmental data into a continuous spatial field. This process facilitates the estimation of health impacts by correlating environmental data with the residential locations of patients. It is noteworthy that the emergence of advanced geo-environmental data products derived from satellite remote sensing technologies now obviates the need for individual researchers to perform such interpolations, offering readily applicable data for health research (8, 9).
Lu and Ren's review article, “Diseases spectrum in the field of spatiotemporal patterns mining of infectious diseases epidemics: a bibliometric and content analysis,” underscores the critical role and extensive utility of GIS technologies and spatial statistics within the domain of infectious disease research. It identifies human mobility and scale effects as pivotal areas for future exploration. Here, we further accentuate the imperative for methodological innovation in the integrated analysis of space-time dynamics, advocating for advancement beyond the traditional compartmentalization of these dimensions (10).
Tang et al. in “A spatiotemporal analysis of the association between carbon productivity, socioeconomics, medical resources, and cardiovascular diseases in southeast rural China,” present a sophisticated case study employing both the Multiscale Geographically Weighted Regression (MGWR) and the Geographically and Temporally Weighted Regression (GTWR) models, which embody the second law of geography, to account for the spatial and temporal heterogeneity inherent in health outcomes and their determinants. Frequency statistics and Bayesian statistics are the two main schools of statistics. The frequentist GTWR model is juxtaposed with the Bayesian Spatiotemporally Varying Coefficients (STVC) model (11, 12), which represents a unified full-map framework for detecting heterogeneous relationships over space and time, and has also been successfully implemented in health and medical geography studies (13).
This Research Topic offers a view of the recent paradigmatic achievements in the intersecting realms of geospatial technologies and health research. Although the compilation here represents but a fraction of the burgeoning field, it is envisaged that the research presented will act as seminal references, stirring academic curiosity and innovation. The advent of big geospatial data has revolutionized the study of human health, imposing new challenges upon the methods employed in geospatial health and medical research. The emerging fields of Geospatial Artificial Intelligence (GeoAI), geospatial causal inference (14), coupling of individual and regional health data (15), population movement trajectories, scale effects, and multi-scale spatiotemporal heterogeneity modeling (10), are just a few areas where further methodological evolution is both anticipated and essential.
In anticipation of the future, we invite scholars—including public health experts, GIS practitioners, and spatial statisticians—to engage with these challenges, fostering the development of novel methodologies designed to address the sophisticated and intricate scientific queries that lie at the intersection of public health, geography, and big data analytics. It is through such interdisciplinary collaboration and innovation that the field will continue to expand and contribute to the betterment of global health outcomes.
Author contributions
CS: Conceptualization, Funding acquisition, Project administration, Writing—original draft. XW: Funding acquisition, Writing—review & editing. EG: Writing—review & editing. XS: Writing—review & editing. JP: Conceptualization, Funding acquisition, Writing—review & editing.
Funding
The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The study was supported by grants from the National Natural Science Foundation of China (42071379, 72374149, 72204175, 72104158, 72104159, and 41701448), Sichuan Science and Technology Department (2022NSFSC0642), Medical Science and Technology Project of Sichuan Provincial Health Commission (21PJ067), Open Project of the Think Tank to Construct Ecological Protective Screens for the Upper Reaches of the Yangtze and Yellow Rivers (202210), and Fund for Introducing Talents of Sichuan University (YJ202157).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note
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References
1. Shi X, Lin H. Introduction: advances in geospatial analysis for health research[J]. Annals GIS. (2020) 26:217–8. doi: 10.1080/19475683.2020.1800900
2. Wang F. Why public health needs GIS: a methodological overview. Ann GIS. (2020) 26:1–12. doi: 10.1080/19475683.2019.1702099
3. Pan J, Chen C. Reducing universal health coverage regional disparities in China. Lancet Public Health. (2022) 7:e985–6. doi: 10.1016/S2468-2667(22)00256-0
4. Wang X, Yang H, Duan Z, Pan J. Spatial accessibility of primary health care in China: a case study in Sichuan Province. Soc Sci Med. (2018) 209:14–24. doi: 10.1016/j.socscimed.2018.05.023
5. Pan J, Deng Y, Yang Y, Zhang Y. Location-allocation modelling for rational health planning: applying a two-step optimization approach to evaluate the spatial accessibility improvement of newly added tertiary hospitals in a metropolitan city of China. Soc Sci Med. (2023) 338:116296. doi: 10.1016/j.socscimed.2023.116296
6. Wang X, Seyler BC, Han W, Pan J. An integrated analysis of spatial access to the three-tier healthcare delivery system in China: a case study of Hainan Island. Int J Equity Health. (2021) 20:1–15. doi: 10.1186/s12939-021-01401-w
7. Wang F. Measurement, optimization, and impact of health care accessibility: a methodological review. Ann Assoc Am Geogr. (2012) 102:1104–12. doi: 10.1080/00045608.2012.657146
8. Cai M, Lin X, Wang X, Zhang S, Wang C, Zhang Z, et al. Long-term exposure to ambient fine particulate matter chemical composition and in-hospital case fatality among patients with stroke in China. Lancet Reg Health West Pac. (2023) 32:100679. doi: 10.1016/j.lanwpc.2022.100679
9. Ge E, Gao J, Wei X, Ren Z, Wei J, Liu X, et al. Effect modification of greenness on PM25 associated all-cause mortality in a multidrug-resistant tuberculosis cohort. Thorax. (2022) 77:1202–9. doi: 10.1136/thoraxjnl-2020-216819
10. Song C, Yin H, Shi X, Xie M, Yang S, Zhou J, et al. Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities. Int J Disast Risk Reduct. (2022) 77:103078. doi: 10.1016/j.ijdrr.2022.103078
11. Song C, Shi X, Bo Y, Wang J, Wang Y, Huang D. Exploring spatiotemporal nonstationary effects of climate factors on hand, foot, and mouth disease using Bayesian Spatiotemporally Varying Coefficients (STVC) model in Sichuan, China. Sci Total Environ. (2019) 648:550–60. doi: 10.1016/j.scitotenv.2018.08.114
12. Song C, Shi X, Wang J. Spatiotemporally Varying Coefficients (STVC) model: a Bayesian local regression to detect spatial and temporal nonstationarity in variables relationships. Ann GIS. (2020) 26:277–91. doi: 10.1080/19475683.2020.1782469
13. Wan Q, Tang Z, Pan J, Xie M, Wang S, Yin H, et al. Spatiotemporal heterogeneity in associations of national population ageing with socioeconomic and environmental factors at the global scale. J Clean Prod. (2022) 373:133781. doi: 10.1016/j.jclepro.2022.133781
14. Gao B, Yang J, Chen Z, Sugihara G, Li M, Stein A, et al. Causal inference from cross-sectional earth system data with geographical convergent cross mapping. Nat Commun. (2023) 14:5875. doi: 10.1038/s41467-023-41619-6
Keywords: health econometrics, spatial econometrics, spatial health statistics, health and medical geography, Geographic Information Science
Citation: Song C, Wang X, Ge E, Shi X and Pan J (2024) Editorial: Applications of geospatial information technologies and spatial statistics in health services research. Front. Public Health 11:1349985. doi: 10.3389/fpubh.2023.1349985
Received: 05 December 2023; Accepted: 12 December 2023;
Published: 04 January 2024.
Edited and reviewed by: Hai Fang, Peking University, China
Copyright © 2024 Song, Wang, Ge, Shi and Pan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jay Pan, panjie.jay@scu.edu.cn