IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v14y2017i8p883-d107194.html
   My bibliography  Save this article

Predicting Urban Medical Services Demand in China: An Improved Grey Markov Chain Model by Taylor Approximation

Author

Listed:
  • Jinli Duan

    (College of Pharmacy, Fujian University of Traditional Chinese Medicine, No. 1 Qiuyang Road, Fuzhou 350122, Fujian, China
    School of Economics and Management, Fuzhou University, No. 2 Xueyuan Road, Fuzhou 350108, Fujian, China)

  • Feng Jiao

    (Newcastle University Business School, Newcastle University, 5 Barrack Road, Newcastle upon Tyne NE1 4SE, UK)

  • Qishan Zhang

    (School of Economics and Management, Fuzhou University, No. 2 Xueyuan Road, Fuzhou 350108, Fujian, China)

  • Zhibin Lin

    (Newcastle Business School, Northumbria University, Newcastle upon Tyne NE1 8ST, UK)

Abstract

The sharp increase of the aging population has raised the pressure on the current limited medical resources in China. To better allocate resources, a more accurate prediction on medical service demand is very urgently needed. This study aims to improve the prediction on medical services demand in China. To achieve this aim, the study combines Taylor Approximation into the Grey Markov Chain model, and develops a new model named Taylor-Markov Chain GM (1,1) (T-MCGM (1,1)). The new model has been tested by adopting the historical data, which includes the medical service on treatment of diabetes, heart disease, and cerebrovascular disease from 1997 to 2015 in China. The model provides a predication on medical service demand of these three types of disease up to 2022. The results reveal an enormous growth of urban medical service demand in the future. The findings provide practical implications for the Health Administrative Department to allocate medical resources, and help hospitals to manage investments on medical facilities.

Suggested Citation

  • Jinli Duan & Feng Jiao & Qishan Zhang & Zhibin Lin, 2017. "Predicting Urban Medical Services Demand in China: An Improved Grey Markov Chain Model by Taylor Approximation," IJERPH, MDPI, vol. 14(8), pages 1-12, August.
  • Handle: RePEc:gam:jijerp:v:14:y:2017:i:8:p:883-:d:107194
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/14/8/883/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/14/8/883/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Yue & Liang, Liping & Liu, Emma & Chen, Chong & Atkins, Derek, 2016. "Patient choice analysis and demand prediction for a health care diagnostics company," European Journal of Operational Research, Elsevier, vol. 251(1), pages 198-205.
    2. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    3. Deb, Partha & Trivedi, Pravin K, 1997. "Demand for Medical Care by the Elderly: A Finite Mixture Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 313-336, May-June.
    4. Teresa Cardoso & Mónica Oliveira & Ana Barbosa-Póvoa & Stefan Nickel, 2012. "Modeling the demand for long-term care services under uncertain information," Health Care Management Science, Springer, vol. 15(4), pages 385-412, December.
    5. Li-Hui Chen & Tsuei-Yang Guo, 2011. "Forecasting financial crises for an enterprise by using the Grey Markov forecasting model," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(4), pages 911-922, June.
    6. Raffaele Argiento & Alessandra Guglielmi & Ettore Lanzarone & Inad Nawajah, 2016. "A Bayesian framework for describing and predicting the stochastic demand of home care patients," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 254-279, June.
    7. Grossman, Michael, 1972. "On the Concept of Health Capital and the Demand for Health," Journal of Political Economy, University of Chicago Press, vol. 80(2), pages 223-255, March-Apr.
    8. Lei-Chuan Lin & Shan-Yau Wu, 2013. "Analyzing Taiwan IC Assembly Industry by Grey-Markov Forecasting Model," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-6, November.
    9. Yanxia Wang & Qingyun Du & Fu Ren & Shi Liang & De-nan Lin & Qin Tian & Yan Chen & Jia-jia Li, 2014. "Spatio-Temporal Variation and Prediction of Ischemic Heart Disease Hospitalizations in Shenzhen, China," IJERPH, MDPI, vol. 11(5), pages 1-26, May.
    10. Shaghayegh Kordnoori & Hamidreza Mostafaei & Shirin Kordnoori, 2014. "The Application of Fourier Residual Grey Verhulst and Grey Markov Model in Analyzing the Global ICT Development," Hyperion Economic Journal, Faculty of Economic Sciences, Hyperion University of Bucharest, Romania, vol. 2(1), pages 50-60, March.
    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. Liudan Jiao & Bowei Han & Qilin Tan & Yu Zhang & Xiaosen Huo & Liu Wu & Ya Wu, 2024. "An Improved DPSIR-DEA Assessment Model for Urban Resilience: A Case Study of 105 Large Cities in China," Land, MDPI, vol. 13(8), pages 1-23, July.
    2. Zhihui Jia & Xiaotong Wen & Xiaohui Lin & Yixiang Lin & Xuyang Li & Guoqing Li & Zhaokang Yuan, 2021. "Working Hours, Job Burnout, and Subjective Well-Being of Hospital Administrators: An Empirical Study Based on China’s Tertiary Public Hospitals," IJERPH, MDPI, vol. 18(9), pages 1-13, April.
    3. Juan Du & Shuhong Cui & Hong Gao, 2020. "Assessing Productivity Development of Public Hospitals: A Case Study of Shanghai, China," IJERPH, MDPI, vol. 17(18), pages 1-9, September.

    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. Anikó Bíró, 2014. "Supplementary private health insurance and health care utilization of people aged 50+," Empirical Economics, Springer, vol. 46(2), pages 501-524, March.
    2. Timothy J. Halliday, 2008. "Heterogeneity, state dependence and health," Econometrics Journal, Royal Economic Society, vol. 11(3), pages 499-516, November.
    3. Nabanita Datta Gupta & Jane Greve, 2011. "Overweight and obesity and the utilization of primary care physicians," Health Economics, John Wiley & Sons, Ltd., vol. 20(S1), pages 53-67, September.
    4. Stefanie Schurer, 2008. "Discrete Heterogeneity in the Impact of Health Shocks on Labour Market Outcomes," Ruhr Economic Papers 0071, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    5. Harris, Matthew & Kohn, Jennifer, 2015. "Reference dependent utility from health and the demand for medical care," MPRA Paper 61926, University Library of Munich, Germany.
    6. José Murteira & Óscar Lourenço, 2011. "Health care utilization and self-assessed health: specification of bivariate models using copulas," Empirical Economics, Springer, vol. 41(2), pages 447-472, October.
    7. Majo, M.C., 2010. "A microeconometric analysis of health care utilization in Europe," Other publications TiSEM 1cf5fd2f-8146-4ef8-8eb5-e, Tilburg University, School of Economics and Management.
    8. Juergen Jung, 2022. "Estimating transition probabilities between health states using US longitudinal survey data," Empirical Economics, Springer, vol. 63(2), pages 901-943, August.
    9. Sisira Sarma & Wayne Simpson, 2006. "A microeconometric analysis of Canadian health care utilization," Health Economics, John Wiley & Sons, Ltd., vol. 15(3), pages 219-239, March.
    10. Galina Besstremyannaya, 2014. "Heterogeneous effect of coinsurance rate on healthcare costs: generalized finite mixtures and matching estimators," Discussion Papers 14-014, Stanford Institute for Economic Policy Research.
    11. Óscar D. Lourenço & Pedro L. Ferreira, 2005. "Utilization of public health centres in Portugal: effect of time costs and other determinants. Finite mixture models applied to truncated samples," Health Economics, John Wiley & Sons, Ltd., vol. 14(9), pages 939-953, September.
    12. McLeod, Logan, 2011. "A nonparametric vs. latent class model of general practitioner utilization: Evidence from Canada," Journal of Health Economics, Elsevier, vol. 30(6), pages 1261-1279.
    13. María José Suárez & Cristina Muñiz, 2018. "Unobserved heterogeneity in work absence," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(8), pages 1137-1148, November.
    14. Antonio J. Trujillo & John A. Vernon & Laura Rodriguez Wong & Gustavo Angeles, 2005. "Race and Health Disparities Among Seniors in Urban Areas in Brazil," NBER Working Papers 11690, National Bureau of Economic Research, Inc.
    15. Alice Sanwald & Engelbert Theurl, 2017. "Out-of-pocket expenditures for pharmaceuticals: lessons from the Austrian household budget survey," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(4), pages 435-447, May.
    16. Michael D. Creel & Montserrat Farell, 2001. "Likelihood-Based Approaches to Modeling Demand for Medical Care," UFAE and IAE Working Papers 498.01, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    17. Smith, Samantha & Walsh, Brendan & Wren, Maev-Ann & Barron, Steve & Morgenroth, Edgar & Eighan, James & Lyons, Seán, 2019. "Geographic profile of healthcare needs and non-acute healthcare supply in Ireland," Research Series, Economic and Social Research Institute (ESRI), number RS90.
    18. Stefanie Schurer, 2008. "Discrete Heterogeneity in the Impact of Health Shocks on Labour Market Outcomes," Melbourne Institute Working Paper Series wp2008n19, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    19. Antonio Clavero Barranquero & Mª. Luz González Alvarez, 2005. "A survey of econometric models to analyze the demand and utilisation of health care," Hacienda Pública Española / Review of Public Economics, IEF, vol. 173(2), pages 129-162, June.
    20. Monika Sander, 2008. "Is There Migration-Related Inequity in Access to or in the Utilisation of Health Care in Germany?," SOEPpapers on Multidisciplinary Panel Data Research 147, DIW Berlin, The German Socio-Economic Panel (SOEP).

    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:14:y:2017:i:8:p:883-:d:107194. 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.