Assessment of the supply/demand balance of medical resources in Beijing from the perspective of hierarchical diagnosis and treatment

Submitted: 26 July 2023
Accepted: 21 September 2023
Published: 13 October 2023
Abstract Views: 977
PDF: 413
HTML: 82
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

Considering the United Nations’ Sustainable Development Goals (SDGs) and the need for a balanced spatial distribution of urban medical resources capable of perspective of hierarchical diagnosis and treatment, i.e. providing continuous and accessible medical services during potential public health emergencies, we assessed accessibility and service capacity of the three hospital levels in Beijing. Using geographical information systems (GIS) and the two-step floating catchment area method with the street as research unit, we found that there is an over-supply of medical resources in the centre of the city with weaker support in the peripheral areas as manifested by less supply in relation to popular demand of medical services. The spatial distribution of hospitals at all levels and their resources was found to be uneven: 82.4% of the residents can reach a tertiary hospital (a hospital offering advanced specialized medical and health services to multiple regions) within a 15-minute drive; 50.6% can reach a secondary hospital (a hospital offering comprehensive medical and health services to various communities) within a 10-minute drive; and 77.6% can reach a primary hospital (a hospital directly delivering prevention, medical treatment, healthcare, and rehabilitation services to the community of a certain population) within a 15- minute walk. It was noted that the supply/demand balance of medical resources in the tertiary hospitals decreases from the centre to the periphery, while the secondary hospitals show a dual-centre pattern and the primary hospitals a more uneven distribution, with oversupply in the East and the opposite in the Centre. The results of the study provide supplementary decision support for improving the hierarchical diagnosis and treatment system and accelerate the overall deployment of medical resources.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Anselin L, 1995. Local indicators of spatial association-LISA. Geogr Anal 27:93-115. DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Beijing Municipal Bureau of Statistics. Bulletin of the Seventh National Population Census of Beijing (No. 2) (2020) [EB/OL].
Beijing Municipal Commission of Health. Special Plan for Medical and Health Facilities in Beijing (2020-2035) [EB/OL].
Beijing Municipal Commission of Planning and Natural Resources. Beijing Urban Master Plan (2016-2035) [EB/OL].
Beijing Municipal Health Commission Information Center. Compendium of statistics on health work in Beijing in 2020 (2020) [EB/OL].
Bennett WD, 1981. A location-allocation approach to health care facility location: A study of the undoctored population in Lansing, Michigan. Soc Sci Med D Med Geogr 15:305–312. DOI: https://doi.org/10.1016/0160-8002(81)90006-X
Cheng M, Lian Y, 2018. Spatial accessibility of urban medical facilities based on improved potential model: A case study of Yangpu District in Shanghai. Prog Geogr 02:266-275.
Gong S, Gao Y, Zhang F, Mu L, Kang C, Liu Y, 2021. Evaluating healthcare resource inequality in Beijing, China based on an improved spatial accessibility measurement. Transactions in GIS 25:1504–1521. DOI: https://doi.org/10.1111/tgis.12737
Horev T, Pesis-Katz I, Mukamel DB, 2004. Trends in geographic disparities in allocation of health care resources in the US. Health Policy 68:223–232. DOI: https://doi.org/10.1016/j.healthpol.2003.09.011
Li C, Bu P, Fang J, Ma K, Chen A, 2018. Research on Accessibility Evaluation of Medical Service in Xiangtan City Based on Improved Gravity Model. Econ Geogr 12:83-88.
Liang G, Yang X, 2021. Spatial pattern and scale of urban medical facilities in Nanning. J Nanning Normal University (Natural Science Edition) 1:101-106.
Ling T, 2018. Analysis of urban hotspots based on POI data. Master's thesis, Kunming University of Science and Technology.
Liu XT, 2007. General Description of Spatial Accessibility. Urban Transport of China 06:36-43.
Luo W, Wang F, 2003. Measures of Spatial Accessibility to Health Care in a GIS Environment: Synthesis and a Case Study in the Chicago Region. Environ Plann B. Plann Des 30:865–884. DOI: https://doi.org/10.1068/b29120
McGrail MR, Humphreys JS, 2009. Measuring spatial accessibility to primary care in rural areas: Improving the effectiveness of the 2SFCA method. Appl Geogr 29:533–541. DOI: https://doi.org/10.1016/j.apgeog.2008.12.003
Meng T, Zhang J, 2017. Evaluation of Health Care Service Spatial Accessibility and the Analysis of the Spatial Distribution Characteristics in Beijing. Geospatial Information 3:62-65+11.
Moran PAP, 1950. Notes on Continuous Stochastic Phenomena. Biometrika 37:17–23. DOI: https://doi.org/10.1093/biomet/37.1-2.17
Peng J, Luo J, Xiong J, Zheng W, 2012. Review of Domestic and Foreign Research on the Basic Public Service Accessibility. Areal Res Devel 2:20-25.
Radke J, Mu L, 2000. Spatial Decompositions, Modeling and Mapping Service Regions to Predict Access to Social Programs. Ann GIS 6:105–112. DOI: https://doi.org/10.1080/10824000009480538
Ruiz GF, Zapata JT, Garavito BL, 2013. Colombian health care system: results on equity for five health dimensions, 2003 - 2008. Rev Panam Salud Pública 33:107–115. DOI: https://doi.org/10.1590/S1020-49892013000200005
Schuurman N, Bérubé M, Crooks VA, 2010. Measuring potential spatial access to primary health care physicians using a modified gravity model. Can Geogr 54:29–45. DOI: https://doi.org/10.1111/j.1541-0064.2009.00301.x
State Council of the Central Committee of the Communist Party of China. Healthy China 2030 (2016) [EB/OL].
Utami RKS, Khakhim N, Jatmiko RH, Kurniawan A, Halengkara L, 2022. Gis network analysis to optimize zoning system implementation for public junior high schools in yogyakarta city. IOP Conference Series: Earth and Environmental Science 1089:012035. DOI: https://doi.org/10.1088/1755-1315/1089/1/012035
Wang F, Tang Q, 2013. Planning toward Equal Accessibility to Services: A Quadratic Programming Approach. Environ Plann B. Plann Des 40:195–212. DOI: https://doi.org/10.1068/b37096
Wang Y, 2006. GIS and Voronoi Polygon Based Public Health Care Accessibility Analysis. Geomatics Spatial Inform Technol 3:77-80.
Yip WC-M, Hsiao WC, Chen W, Hu S, Ma J, Maynard A, 2020. Early Appraisal of China’s Huge and Complex Health Care Reforms. World Sci Series Global Health Econ Public Policy 51–83. DOI: https://doi.org/10.1142/9789813236134_0004
Yu W, Ai T, 2015. The Visualization and Analysis of POI Features under Network Space Supported by Kernel Density Estimation. Acta Geodaetica et Cartographica Sinica 1:82-90.
Zhao M, Liu S, Qi W, 2018a. Spatial Differentiation and Influencing Mechanism of Medical Care Accessibility in Beijing: A Migrant Equality Perspective. Chin Geogr Sci 28:353–362. DOI: https://doi.org/10.1007/s11769-018-0950-x
Zhao S, Li N, Liu Y, 2018b. Research on Spatial Distribution Rationality of Medical Resources in Beijing. Contemp Med 22:22-25.

How to Cite

Jiang, Y. ., Cai, X., Wang, Y., Dong, J., & Yang, M. (2023). Assessment of the supply/demand balance of medical resources in Beijing from the perspective of hierarchical diagnosis and treatment. Geospatial Health, 18(2). https://doi.org/10.4081/gh.2023.1228