Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
권호 표지
DOI QR코드

DOI QR Code

Efficiency evaluation of nursing homes in China's eastern areas Based on DEA-Malmquist Model

DEA-Malmquist를 활용한 중국 동부지역 요양원의 효율성 평가에 관한 연구

  • Chu, Ting (The School of Nursing, Zhejiang Chinese Medical University) ;
  • Sim, Jae-yeon (Dept. of Business Management, Sehan University)
  • 초정 (절강중의약대학교 간호학과) ;
  • 심재연 (세한대학교 경영학과)
  • Received : 2021.04.30
  • Accepted : 2021.07.20
  • Published : 2021.07.28
Download PDF
⟨ Previous Next ⟩

Abstract

Nursing home plays a role in providing elderly care in the context of China's rapid population aging, but little understanding of the efficiency of the nursing homes. In this paper, we investigated the efficiency in nursing homes using Data Envelopment Analysis (DEA) and Malmquist index (MPI) for the modeling of the number of nursing home beds, fixed assets, and medical personnel as input variables, and the number of elderly people of self-care, the number of elderly people of partial self-care, the number of bed-ridden elderly people and the income of nursing homes as output variables. Stratification analysis showed that the top two provinces in the DEA-CCR yield were Beijing and Shanghai in the five-year survey period. Four provinces (Beijing, Jiangsu, Shandong, and Shanghai) scored 1.00 in terms of DEA-BCC yield. The MPI analysis showed that Hainan ranked the highest five-year average in the included provinces. In terms of resource utilization, internal management, operation scale, and other aspects, the nursing homes in the provinces with high-efficiency evaluation results show high efficiency and technological progress, whereas the areas with low-efficiency evaluation showed a feature of the improving technical efficiency.

중국의 급속한 인구 고령화 상황에서 요양원은 노인요양을 제공하는 역할을 하고 있지만 요양원의 효율성에 대한 이해는 거의 없었다. 본 논문은 요양원의 효율성 향상전략을 제안할 목적으로 DEA 및 Malmquist 지수 분석을 활용하여 평가하였다. 효율성 평가지표는 병상의 수, 고정자산, 의료인원의 수를 투입변수로 하고, 자가간호 가능노인의 수, 부분간호 가능노인의 수, 와상노인의 수, 요양원의 수입을 산출변수로 하여, 계층화 분석결과 DEA-CCR에서는 베이징과 상하이가 조사기간 5개년동안 1.00의 결과를 나타냈고, DEA-BCC에서는 4개 지역(베이징, 장쑤, 산둥, 상하이)이 가장 높은 결과를 나타났다. Malmquist 지수(MPI)에서는 하이난이 가장 높은 것으로 나타났다. 효율성 평가 결과가 높은 성(省)의 요양원은 자원활용, 내부관리, 경영규모 등에서 효율적이고 기술 진보적인 반면, 효율성 평가가 낮은 지역의 요양원은 기술효율이 높아지는 특징을 보였다.

Keywords

Acknowledgement

This Paper was supported by the Sehan University Fund in 2021.

References

  1. G. Mu. (2000). The transformation and prospects of traditional Chinese old-age care methods. Journal of the Renmin University of China, 5, 39-44.
  2. Ministry of Civil Affairs of the People's Republic of China. (2020). 2019 Statistical Bulletin of Civil Affairs Development. (in Chinese) http://www.mca.gov.cn/article/sj/tjgb/
  3. United Nations. (2015). World Population Outlook. http://esa.un.org/unpd/wpp/
  4. X. Yang. (2018). An Empirical Analysis of the Efficiency of China's Elderly Service Industry and Its Influencing Factors. Zhejiang : Zhejiang Gongshang University.
  5. Y. Zeng. (2010). Discussion on the Characteristics and Countermeasures of the "Two high Three big" of China's Population Aging. Population and Economy, 5, 3-9.
  6. Q. Wang. (2017). DEA method and resource allocation problem research. Beijing: China Social Sciences Press, p. 3-7.
  7. Ministry of Civil Affairs of the People's Republic of China. (2020). 2019 China Civil Affairs Statistical Yearbook. http://www.mca.gov.cn/article/sj/
  8. A. Tran, K. H. Nguyen, L. Gray & T. Comans. (2019). A Systematic Literature Review of Efficiency Measurement in Nursing Homes. International journal of environmental research and public health, 16(12), 2186. https://doi.org/10.3390/ijerph16122186
  9. X. Wang. (2015). The status quo and future development models of nursing homes in China. Chinese market, 51, 175,178. DOI:10.13939/j.cnki.zgsc.2015.51.175. (in Chinese)
  10. R. Bhat, B. B. Verma & E. Reuben. (2001). Data Envelopment Analysis (DEA). Journal of Health Management, 3(2), 309-328. https://doi.org/10.1177/097206340100300207
  11. D. G. Shimshak, M. L. Lenard & R. K. Klimberg. (2009). Incorporating Quality into Data Envelopment Analysis of Nursing Home Performance: A Case Study. Omega, 37(3), 672-685. https://doi.org/10.1016/j.omega.2008.05.004
  12. T. R. Sexton, A. M. Leiken, S. Sleeper & A. F. Coburn. (1989). The impact of prospective reimbursement on nursing home efficiency. Medical care, 27(2), 154-163. https://doi.org/10.1097/00005650-198902000-00006
  13. B. Hollingsworth, P. J. Dawson & N. Maniadakis. (1999). Efficiency measurement of health care: a review of nonparametric methods and applications. Health care management science, 2(3), 161-172. https://doi.org/10.1023/a:1019087828488
  14. Y. A. Ozcan, S. E. Wogen & L. W. Mau. (1998). Efficiency Evaluation of Skilled Nursing Facilities. Journal of Medical Systems, 22(4), 211-224. https://doi.org/10.1023/A:1022657600192
  15. J. A. Nyman, D. L. Bricker & D. Link. (1990). Technical efficiency in nursing homes. Medical care, 28(6), 541-551. https://doi.org/10.1097/00005650-199006000-00006
  16. J. Fizel & T. Nunnikhoven. (1992). Technical Efficiency of For-Profit and Non-Profit Nursing Homes. Managerial and Decision Economics, 13(5), 429-439. Retrieved March 28, 2021. http://www.jstor.org/stable/2487777 https://doi.org/10.1002/mde.4090130507
  17. P. Kooreman. (1994). Nursing home care in The Netherlands: a nonparametric efficiency analysis. Journal of health economics, 13(3), 301-346. https://doi.org/10.1016/0167-6296(94)90029-9
  18. M. D. Rosko, J. A. Chilingerian, J. S. Zinn & W. E. Aaronson. (1995). The effects of ownership, operating environment, and strategic choices on nursing home efficiency. Medical care, 33(10), 1001-1021. https://doi.org/10.1097/00005650-199510000-00003
  19. G. Garavaglia, E. Lettieri, T. Agasisti & S. Lopez. (2011). Efficiency and quality of care in nursing homes: an Italian case study. Health care management science, 14(1), 22-35. https://doi.org/10.1007/s10729-010-9139-2
  20. R. Dulal. (2018). Technical efficiency of nursing homes: do five-star quality ratings matter?. Health care management science, 21(3), 393-400. https://doi.org/10.1007/s10729-017-9392-8
  21. M. A. Bjorkgren, U. Hakkinen & M. Linna. (2001). Measuring efficiency of long-term care units in Finland. Health care management science, 4(3), 193-200. https://doi.org/10.1023/a:1011444815466
  22. Y. Zhou & Z. Chai. (2015). Research on the Efficiency Evaluation of Ningbo Elderly Care Institutions. Journal of Ningbo University (Humanities Edition), 3, 79-84.
  23. J. Ren. (2016). A Study on the Efficiency of Institutional Elderly Care Service-Taking Xiamen City as an Example. Population and Economy, 2, 58-68.
  24. T. Huang. (2017). Analysis on the Operational Efficiency of Ningbo Elderly Care Institutions. Zhejiang : Ningbo University.
  25. Y. Tian, S. Cui & S. Yang. (2018). Research on the implementation effect of support policies for elderly care institutions-Based on the survey and analysis of 45 elderly care institutions in Shandong Province. Journal of Shandong University (Philosophy and Social Sciences Edition), 3, 79-89. DOI:10.3969/j.issn.1001-9839.2018.03.009.
  26. Y. Zeng, L. Zhang, J. Yan & Y. Fang. (2019). Research on the Service Efficiency and Influencing Factors of Elderly Care Institutions in Xiamen City Based on DEA. Chinese Journal of Health Statistics, 36(1), 13-17. (in Chinese)
  27. I. K. Kleinsorge & D. F. Karney. (1992). Management of nursing homes using data envelopment analysis. Socio-economic planning sciences, 26(1), 57-71. https://doi.org/10.1016/0038-0121(92)90026-2
  28. N. J. Zhang, L. Unruh & T. T. Wan. (2008). Has the Medicare prospective payment system led to increased nursing home efficiency?. Health services research, 43(3), 1043-1061. https://doi.org/10.1111/j.1475-6773.2007.00798.x
  29. M. L. Lenard & D. G. Shimshak. (2009). Benchmarking nursing home performance at the state level. Health services management research, 22(2), 51-61. https://doi.org/10.1258/hsmr.2008.008006
  30. A. Min, C. G. Park & L. D. Scott. (2016). Evaluating Technical Efficiency of Nursing Care Using Data Envelopment Analysis and Multilevel Modeling. Western journal of nursing research, 38(11), 1489-1508. https://doi.org/10.1177/0193945916650199
  31. M. D. Masri & E. Z. Asbu. (2018). Productivity change of national health systems in the WHO Eastern Mediterranean region: application of DEA-based Malmquist productivity index. Global health research and policy, 3, 22. https://doi.org/10.1186/s41256-018-0077-8
  32. N. G. Castle & J. C. Ferguson. (2010). What is nursing home quality and how is it measured?. The Gerontologist, 50(4), 426-442. https://doi.org/10.1093/geront/gnq052
  33. Ministry of Civil Affairs of the People's Republic of China. (2015-19). 2014-18 China Civil Affairs Statistical Yearbook. (in Chinese) http://www.mca.gov.cn/article/sj/
  34. R. Weech-Maldonado et al. (2019). High Medicaid nursing homes: Organizational and market factors associated with financial performance. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 56, 0046958018825061. https://doi.org/10.1177/0046958018825061
  35. National Development and Reform Commission of China. (2014). Notice on accelerating the construction of health and elderly services project. (in Chinese) https://www.ndrc.gov.cn/xxgk/zcfb/tz/201410/t20141010_964249.html