IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i3p1664-d1038231.html
   My bibliography  Save this article

Temporal-Spatial Evolution and Trend Prediction of the Supply Efficiency of Primary Medical Health Service—An Empirical Study Based on Central and Western Regions of China

Author

Listed:
  • Fang Wu

    (School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing 211198, China)

  • Mingyao Gu

    (School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing 211198, China)

  • Chenming Zhu

    (School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing 211198, China)

  • Yingna Qu

    (School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing 211198, China)

Abstract

China has established a comprehensive primary medical health service system, but the development of primary medical health services in the central and western regions is still unbalanced and insufficient. Based on data from 2010 to 2019, this paper constructs a super efficiency Slack-Based Measure model to calculate the supply efficiency of primary medical health services in 20 provinces and cities in central and western China. Using Kernel density estimation and Markov chain analysis, this paper further analyzes the spatial-temporal evolution of the supply efficiency of primary medical health services in central and western China, and also predicts the future development distribution through the limiting distribution of Markov chain to provide a theoretical basis for promoting the sinking of high-quality medical resources to the primary level. The results show that firstly, during the observation period, the center of the Kernel density curve moves to the left, and the main peak value decreases continuously. The main diagonal elements of the traditional Markov transition probability matrix are 0.7872, 0.5172, 0.8353, and 0.7368 respectively, which are significantly larger than other elements. Secondly, when adjacent to low state and high state, it will develop into convergence distributions of 0.7251 and 0.8243. The supply efficiency of primary medical health services in central and western China has the characteristics of high (Ningxia) and low (Shaanxi) aggregation respectively, but the aggregation trend is weakened. Thirdly, the supply efficiency of health services has the stability of keeping its own state unchanged, but the transition of state can still occur. The long-term development of the current trend cannot break the distribution characteristics of the high and low clusters, the efficiency will show a downward trend in the next 10–20 years, and still the problem of uneven long-term development emerges.

Suggested Citation

  • Fang Wu & Mingyao Gu & Chenming Zhu & Yingna Qu, 2023. "Temporal-Spatial Evolution and Trend Prediction of the Supply Efficiency of Primary Medical Health Service—An Empirical Study Based on Central and Western Regions of China," IJERPH, MDPI, vol. 20(3), pages 1-23, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:1664-:d:1038231
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/3/1664/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/3/1664/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nikolaos Oikonomou & Yannis Tountas & Argiris Mariolis & Kyriakos Souliotis & Kostas Athanasakis & John Kyriopoulos, 2016. "Measuring the efficiency of the Greek rural primary health care using a restricted DEA model; the case of southern and western Greece," Health Care Management Science, Springer, vol. 19(4), pages 313-325, December.
    2. Rouven Edgar Haschka & Katharina Schley & Helmut Herwartz, 2020. "Provision of health care services and regional diversity in Germany: insights from a Bayesian health frontier analysis with spatial dependencies," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(1), pages 55-71, February.
    3. A. Charnes & W. W. Cooper & E. Rhodes, 1981. "Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through," Management Science, INFORMS, vol. 27(6), pages 668-697, June.
    4. Xiaorong Jiang & Wei Wei & Shenglan Wang & Tao Zhang & Chengpeng Lu, 2021. "Effects of COVID-19 on Urban Population Flow in China," IJERPH, MDPI, vol. 18(4), pages 1-14, February.
    5. Shunsuke Shinagawa & Eiji Tsuzuki, 2019. "Policy Lag and Sustained Growth," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 5(3), pages 403-431, October.
    6. Qinde Wu & Xianyu Xie & Wenbin Liu & Yong Wu, 2022. "Implementation efficiency of the hierarchical diagnosis and treatment system in China: A case study of primary medical and health institutions in Fujian province," International Journal of Health Planning and Management, Wiley Blackwell, vol. 37(1), pages 214-227, January.
    7. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    8. Xinyu Zhang & Lin Zhao & Zhuang Cui & Yaogang Wang, 2015. "Study on Equity and Efficiency of Health Resources and Services Based on Key Indicators in China," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-15, December.
    9. Wei Fang & Pengli An & Siyao Liu & Xueyong Liu, 2021. "Evolution Characteristics and Regional Roles’ Influencing Factors of Interprovincial Population Mobility Network in China," Complexity, Hindawi, vol. 2021, pages 1-11, May.
    10. Qian Liu & Bo Li & Muhammad Mohiuddin, 2018. "Prediction and Decomposition of Efficiency Differences in Chinese Provincial Community Health Services," IJERPH, MDPI, vol. 15(10), pages 1-14, October.
    11. Łukasz Lechowski & Angelika Jasion, 2021. "Spatial Accessibility of Primary Health Care in Rural Areas in Poland," IJERPH, MDPI, vol. 18(17), pages 1-16, September.
    12. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    13. Kontodimopoulos, Nick & Nanos, Panagiotis & Niakas, Dimitris, 2006. "Balancing efficiency of health services and equity of access in remote areas in Greece," Health Policy, Elsevier, vol. 76(1), pages 49-57, March.
    14. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    15. Julie Le Gallo, 2004. "Space-Time Analysis of GDP Disparities among European Regions: A Markov Chains Approach," International Regional Science Review, , vol. 27(2), pages 138-163, April.
    16. Anastasios Trakakis & Miltiadis Nektarios & Styliani Tziaferi & Panagiotis Prezerakos, 2022. "Evaluation of the Efficiency in Public Health Centers in Greece Regarding the Human Resources Occupied: A Bootstrap Data Envelopment Analysis Application," IJERPH, MDPI, vol. 19(3), pages 1-13, January.
    17. Jon Chilingerian & H. David Sherman, 1997. "DEA and primary care physician report cards: Deriving preferred practice cones from managed care service concepts and operating strategies," Annals of Operations Research, Springer, vol. 73(0), pages 35-66, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yanlong Guo & Xingmeng Ma & Yelin Zhu & Denghang Chen & Han Zhang, 2023. "Research on Driving Factors of Forest Ecological Security: Evidence from 12 Provincial Administrative Regions in Western China," Sustainability, MDPI, vol. 15(6), pages 1-21, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xu Zhang & Huaping Sun & Taohong Wang, 2022. "Impact of Financial Inclusion on the Efficiency of Carbon Emissions: Evidence from 30 Provinces in China," Energies, MDPI, vol. 15(19), pages 1-15, October.
    2. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    3. Qian Liu & Bo Li & Muhammad Mohiuddin, 2018. "Prediction and Decomposition of Efficiency Differences in Chinese Provincial Community Health Services," IJERPH, MDPI, vol. 15(10), pages 1-14, October.
    4. Necmi Kemal Avkiran, 2017. "An illustration of multiple-stakeholder perspective using a survey across Australia, China and Japan," Annals of Operations Research, Springer, vol. 248(1), pages 93-121, January.
    5. Chia-Nan Wang & Quoc-Chien Luu & Thi-Kim-Lien Nguyen & Jen-Der Day, 2019. "Assessing Bank Performance Using Dynamic SBM Model," Mathematics, MDPI, vol. 7(1), pages 1-13, January.
    6. Avkiran, Necmi K., 2011. "Association of DEA super-efficiency estimates with financial ratios: Investigating the case for Chinese banks," Omega, Elsevier, vol. 39(3), pages 323-334, June.
    7. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    8. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499.
    9. Ashrafi, Ali & Seow, Hsin-Vonn & Lee, Lai Soon & Lee, Chew Ging, 2013. "The efficiency of the hotel industry in Singapore," Tourism Management, Elsevier, vol. 37(C), pages 31-34.
    10. Büschken, Joachim, 2009. "When does data envelopment analysis outperform a naïve efficiency measurement model?," European Journal of Operational Research, Elsevier, vol. 192(2), pages 647-657, January.
    11. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    12. Ruijing Zheng & Yu Cheng & Haimeng Liu & Wei Chen & Xiaodong Chen & Yaping Wang, 2022. "The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation," IJERPH, MDPI, vol. 19(15), pages 1-22, July.
    13. Muliaman Hadad & Maximilian Hall & Karligash Kenjegalieva & Wimboh Santoso & Richard Simper, 2011. "Banking efficiency and stock market performance: an analysis of listed Indonesian banks," Review of Quantitative Finance and Accounting, Springer, vol. 37(1), pages 1-20, July.
    14. Jo, Ah-Hyun & Chang, Young-Tae, 2023. "The effect of airport efficiency on air traffic, using DEA and multilateral resistance terms gravity models," Journal of Air Transport Management, Elsevier, vol. 108(C).
    15. Hongli Liu & Xiaoyu Yan & Jinhua Cheng & Jun Zhang & Yan Bu, 2021. "Driving Factors for the Spatiotemporal Heterogeneity in Technical Efficiency of China’s New Energy Industry," Energies, MDPI, vol. 14(14), pages 1-21, July.
    16. Yi-Chung Hsu, 2014. "Efficiency in government health spending: a super slacks-based model," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(1), pages 111-126, January.
    17. Mousavi, Mohammad M. & Ouenniche, Jamal & Xu, Bing, 2015. "Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 64-75.
    18. Fan Wang & Lili Feng & Jin Li & Lin Wang, 2020. "Environmental Regulation, Tenure Length of Officials, and Green Innovation of Enterprises," IJERPH, MDPI, vol. 17(7), pages 1-16, March.
    19. Ningyi Liu & Yongyu Wang, 2022. "Urban Agglomeration Ecological Welfare Performance and Spatial Convergence Research in the Yellow River Basin," Land, MDPI, vol. 11(11), pages 1-18, November.
    20. Loske, Dominic & Klumpp, Matthias, 2021. "Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics," International Journal of Production Economics, Elsevier, vol. 241(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:1664-:d:1038231. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.