Top

BMC Health Services Research

Published in:

Open Access 01-12-2020 | Research article

Measuring productivity of healthcare services under environmental constraints: evidence from China

Authors: Jinna Yu, Zhen Liu, Tingting Zhang, Assem Abu Hatab, Jing Lan

Published in: BMC Health Services Research | Issue 1/2020

Abstract

Background

Despite the growing literature on the efficiency and productivity of the Chinese healthcare system, less attention has been given to examining the undesirable outputs linked to healthcare services, including environmental pollution. Taking the atmospheric environmental pollution resulting from the incineration of medical waste as an undesirable output of the healthcare system, this study analyzed the growth and decomposition of Total Factor Productivity (TFP) of healthcare services across 31 Chinese provinces during the period 2005–2016.

Methods

The Meta-frontier undesirable super-efficiency slack-based measure (SBM) model and the Global Malmquist-Luenberger (GML) Index were employed to analyze the growth and decomposition of TFP using the Max DEA software.

Results

The results revealed that the years 2009 and 2015 marked significant changes in TFP of healthcare services in Chinese provinces. During the study period, the rate of technological change (TC) slowly declined, whereas the rate of efficiency change (EC) steadily increased. With the national average being the benchmark, the results indicated that: the TFP of 17 provinces and cities exceeded the average, the EC of 16 provinces and cities exceeded the average, the TC of 9 provinces and cities exceeded the average, and the value in the Technology Gap Ratio (TGR) changes of 13 provinces and cities were above the national average.

Conclusions

(1) The TFP of the healthcare services across China continued to decline slowly during the study period. (2) The effect of technical catch-up in the eastern, central, and western regions of China was significant across the three regions, whereas the effect of technical innovation was negative. (3) The TFP varied considerably among the Chinese provinces. These findings suggest that, under existing environmental constraints, relevant government departments should improve technical innovation in the supply of healthcare services and medical waste treatment, increase technical efficiency in the factor of healthcare production, strengthen regional health planning, and balance the development of regional healthcare.

As China has been accounting for the fixed assets of medical and health institutions since 2005, 2005–2016 was chosen as the research period, with the corresponding yearbook year being 2006–2017.

The eastern region includes 11 provinces and cities: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. The western region includes 12 provinces and cities: Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. The central region includes 8 provinces and cities: Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan.

The hospitals in this study include general hospitals, traditional Chinese medicine hospitals, hospitals of traditional Chinese and Western medicine, ethnic hospitals, specialized hospitals, nursing homes, and township health centers.

The environmental pollution caused by the provision of medical services is beyond air pollution and water pollution. However, these types of pollution are perceived to be the most important ones, which have a higher availability of data than others. Moreover, according to the “Hospital Pollutant Generation, Emission Coefficient Manual” of the “First National Pollution Source Survey of Urban Living Sources Sewage Coefficient Manual”, the main kinds of pollutants produced by hospitals are medical waste and sewage. Therefore, these two types of pollutants are of primary concern in this study.

The specific calculation formula is not detailed here. Details can be found in the “2006 IPCC National Greenhouse Gas Inventory Guide” and “Hospital Pollutant Generation, Emission Coefficient Manual” of the “First National Pollution Source Survey of Urban Livelihood Emission Coefficient Manual”.

Some of the literature also refers to is as the intertemporal frontier of a group or a cluster of groups. It refers to the envelope line at the forefront of the current technology of a group of all periods; thus, it is correct to use the forefront of the global production technology of groups to express it.

Hu A, Yan Y, Tang X. Green development. In: Hu A, Yan Y, Tang X, editors. Xi Jinping's new development philosophy: Singapore, Springer Singapore; 2018. p. 59–71. https://doi.org/10.1007/978-981-10-7736-4_4.

Wang D. Pormoting green transformation of medical model. Health News, 2016-02-29(006). [In Chinese] Available from: http://szb.jkb.com.cn/jkbpaper/html/2016-02/29/node_7.htm. Accessed 18 July 2020.

Wang M-l, H-q F, H-b T, et al. Bootstrapping data envelopment analysis of efficiency and productivity of county public hospitals in eastern, central, and Western China after the public hospital reform. Curr Med Sci. 2017;37(5):681–92. https://doi.org/10.1007/s11596-017-1789-6.CrossRef

Awodele O, Adewoye AA, Oparah AC. Assessment of medical waste management in seven hospitals in Lagos, Nigeria. BMC Public Health. 2016;16:269. https://doi.org/10.1186/s12889-016-2916-1.CrossRefPubMedPubMedCentral

Adnane MI, Beklacem K, Abdelkarim E, Mohamed B. Medical waste management: a case study of the Souss-Massa-Drâa region, Morocco. J Environ Prot. 2013;4:914–9. https://doi.org/10.4236/jep.2013.49105.CrossRef

Patience Aseweh Abor. Medical waste management at Tyberberg hospital in the Western cape South Africa. 2007.

Dehghani MH, Azam K, Changani F, Dehghani EF. Assessment of medical waste management in Educational Hospitals of Tehran University Medical Science. Iran J Environ Health Sci Eng. 2008;5(2):131–6.

Hossain MS, Santhanam A, Nik Norulaini NA, Omar AK. Clinical solid waste management practices and its impact on human health and environment. Waste Manag. 2011;31(4):754–66.CrossRef

Patwary MA, O’Hare WT, Street G, Elahi KM, Hossain SS, Sarke MH. Country report: quantitative assessment of medical waste generation in the Capital City of Bangladesh. Waste Manag. 2009;29(8):2392–7. https://doi.org/10.1016/j.wasman.2009.03.021.CrossRefPubMed

10.

Tamplin SA, Davidson D, Powis B, O’Leary Z. Issues and option for the safe destruction and disposal of used injection materials. Waste Manag. 2005;25(6):655–65. https://doi.org/10.1016/j.wasman.2004.07.007.CrossRefPubMed

11.

Ma Y, Lin X, Wu A, Huang Q, Li X, Yan J. Suggested guidelines for emergency treatment of medical waste during COVID-19: Chinese experience. Waste Dispos Sustain Energy. Published 03 June 2020: doi: https://doi.org/10.1007/s42768-020-00039-8.

12.

Jiang X, Li Y, Yan J. Hazardous waste incineration in a rotary kiln: a review. Waste Dispos Sustain Energy. 2019; 1:3–37. doi: https://doi.org/10.1007/s42768-019-00001-3.

13.

Jiang S, Min R, Fang P-Q. The impact of healthcare reform on the efficiency of public county hospitals in China. BMC Health Serv Res. 2017;17(1):838. https://doi.org/10.1186/s12913-017-2780-4.CrossRefPubMedPubMedCentral

14.

Goyal J, Singh M, Singh R, Aggarwal A. Efficiency and technology gaps in Indian banking sector: Application of meta-frontier directional distance function DEA approach. J Financ Data Sci. 2019;5(3):156–72. https://doi.org/10.1016/j.jfds.2018.08.002.CrossRef

15.

Zhang N, Choi Y. Total-factor carbon emission performance of fossil fuel power plants in China: A metafrontier non-radial Malmquist index analysis. Energ Econ. 2013:40, 549–559. https://doi.org/10.1016/j.eneco.2013.08.012.

16.

Cantor VJM, Poh KL. Integrated analysis of healthcare efficiency: a systematic review. J Med Syst. 2017;42(1):8. https://doi.org/10.1007/S10916-017-0848-7.CrossRefPubMed

17.

Bogetoft P, Otto L. Benchmarking with DEA, SFA, and R. New York: Springer; 2011. p. 351. https://doi.org/10.1007/978-1-4419-7961-2.CrossRef

18.

Wu J, Sun J, Song M, Liang L. A ranking method for DMUs with interval data based on dea cross-efficiency evaluation and TOPSIS. J Syst Sci Syst Eng. 2013;22(2):191–201. https://doi.org/10.1007/s11518-013-5216-7.CrossRef

19.

Zhao L, Guo Y, Zhang Y, Liu F. Study on efficiency of medical and health institutions at home and abroad based on data envelopment analysis. Soft Sci Health. 2017;31(9):10–3,18. [In Chinese]. https://doi.org/10.3969/j.issn.1003-2800.2017.09.003.CrossRef

20.

Sherman H. Measurement of hospital technical efficiency: a comparative evaluation of data envelopment analysis and other approaches for locating inefficiency in health care organizations. United States: PhD Thesis, Harvard University; 1981.

21.

Jola-Sanchez AF, Pedraza-Martinez AJ, Bretthauer KM, Britto RA. Effect of armed conflicts on humanitarian operations: Total factor productivity and efficiency of rural hospitals. J Oper Manag. 2016;45:73–85. https://doi.org/10.1016/j.jom.2016.06.002.CrossRef

22.

Zhang X, Tone K, Lu Y. Impact of the local public hospital reform on the efficiency of medium-sized hospitals in Japan: an improved slacks-based measure data envelopment analysis approach. Health Serv Res. 2018;53(2):896–918. https://doi.org/10.1111/1475-6773.12676.CrossRefPubMed

23.

Li N-N, Wang C-H, Ni H, Wang H. Efficiency and productivity of county-level public hospitals based on the data envelopment analysis model and Malmquist index in Anhui, China. Chin Med J (Engl). 2017;130(23):2836–43. https://doi.org/10.4103/0366-6999.219148.CrossRef

24.

Ravangard R, Hatam N, Teimourizad A, Jafari A. Factors affecting the technical efficiency of health systems: A case study of Economic Cooperation Organization (ECO) countries (2004–10). Int J Health Policy Manag. 2014;3(2):63–9 Available from: http://europepmc.org/abstract/MED/25114944.CrossRef

25.

Ozgen H, Ozcan YA. A national study of efficiency for dialysis centers: an examination of market competition and facility characteristics for production of multiple dialysis outputs. Health Serv Res. 2002;37(3):711–32. https://doi.org/10.1111/1475-6773.00045.CrossRefPubMedPubMedCentral

26.

Chilingerian JA, David SH. DEA and primary care physician report cards: deriving preferred practice cones from managed care service concepts and operating strategies. Ann Oper Res. 1997;73(0):35–66. https://doi.org/10.1023/A:1018993515090.CrossRef

27.

Du J, Wang J, Chen Y, Chou S-Y, Zhu J. Incorporating health outcomes in Pennsylvania hospital efficiency: an additive super-efficiency DEA approach. Ann Oper Res. 2014;1(1):161–72. https://doi.org/10.1007/s10479-011-0838-y.CrossRef

28.

Chilingerian JA, Sherman HD. Health-care applications: from hospitals to physicians, from productive efficiency to quality Frontiers. In: Cooper WW, Seiford LM, Zhu J, editors. Handbook on data envelopment analysis. Boston: Springer US; 2011. p. 445–93. https://doi.org/10.1007/978-1-4419-6151-8_16.CrossRef

29.

Kounetas K, Papathanassopoulos F. How efficient are Greek hospitals? A case study using a double bootstrap DEA approach. Eur J Health Econ. 2013;14(6):979–94. https://doi.org/10.1007/s10198-012-0446-z.CrossRefPubMed

30.

Ni Luasa S, Dineen D, Zieba M. Technical and scale efficiency in public and private Irish nursing homes – a bootstrap DEA approach. Health care Manag Sci. 2018;21(3):326–47. https://doi.org/10.1007/s10729-016-9389-8.CrossRefPubMed

31.

Costantino N, Dotoli M, Epicoco N, Falagario M, Sciancalepore F, Editors. Using cross-efficiency fuzzy data envelopment analysis for healthcare facilities performance evaluation under uncertainty. 2013 IEEE international conference on systems, man, and cybernetics; 2013 13-16 Oct. 2013. doi: https://doi.org/10.1109/SMC.2013.160.

32.

Chilingerian JA, David SH. Benchmarking physician practice patterns with DEA: a multi-stage approach for cost containment. Ann Oper Res. 1996;67(1):83–116. https://doi.org/10.1007/BF02187025.CrossRef

33.

O'Neill L. Multifactor efficiency in Data Envelopment Analysis with an application to urban hospitals. Health Care Manag Sci. 1998;1(1):19–27. https://doi.org/10.1023/A:1019030215768.CrossRefPubMed

34.

Xenos P, Yfantopoulos J, Nektarios M, Polyzos N, Tinios P, Constantopoulos A. Efficiency and productivity assessment of public hospitals in Greece during the crisis period 2009–2012. Cost Effect Resour A. 2017;15(1):6. https://doi.org/10.1186/s12962-017-0068-5.CrossRef

35.

Mujasi PN, Asbu EZ, Puigjunoy J. How efficient are referral hospitals in Uganda? A data envelopment analysis and tobit regression approach [J]. BMC Health Serv Res. 2016;16(1):230. https://doi.org/10.1186/s12913-016-1472-9.CrossRefPubMedPubMedCentral

36.

Samsudin S, Jaafar AS, Applanaidu SD, Ali J, Majid R. Are public hospitals in Malaysia efficient? An application of DEA and Tobit analysis. Southeast Asian J Econ. 2016;4(2):1–20 Avaliale from: https://www.tci-thaijo.org/index.php/saje/article/view/72966.

37.

Guo H, Zhao Y, Niu T, Tsui K-L. Hong Kong Hospital Authority resource efficiency evaluation: via a novel DEA-Malmquist model and Tobit regression model. Plos One. 2017;12(9):e0184211. https://doi.org/10.1371/journal.pone.0184211.CrossRefPubMedPubMedCentral

38.

Zhang Y, Wang Q, Jiang T, Wang J. Equity and efficiency of primary health care resource allocation in mainland China. Int J Equity Health. 2018;17:140. https://doi.org/10.1186/s12939-018-0851-8.CrossRefPubMedPubMedCentral

39.

Xu X, Zhou L, Antwi HA, Chen X. Evaluation of health resource utilization efficiency in community health centers of Jiangsu Province, China. Hum Resour Health. 2018;16(1):13. https://doi.org/10.1186/s12960-018-0275-y.CrossRefPubMedPubMedCentral

40.

Wang X, Luo H, Qin X, Feng J, Gao H, Feng Q. Evaluation of performance and impacts of maternal and child health hospital services using data envelopment analysis in Guangxi Zhuang autonomous region, China: a comparison study among poverty and non-poverty county level hospitals. Int J Equity Health. 2016;15(1):131. https://doi.org/10.1186/s12939-016-0420-y.CrossRefPubMedPubMedCentral

41.

Hu H-H, Qi Q, Yang C-H. Evaluation of China's regional hospital efficiency: DEA approach with undesirable output. J Oper Res Soc. 2012;(6):63, 715–725. https://doi.org/10.1057/jors.2011.77.

42.

Färe R, Grosskopf S, Lindgren B, Roos P. Productivity changes in Swedish pharamacies 1980–1989: a non-parametric Malmquist approach. J Prod Anal. 1992;3(1):85–101. https://doi.org/10.1007/BF00158770.CrossRef

43.

Chung YH, Färe R, Grosskopf S. Productivity and Undesirable Outputs: A Directional Distance Function Approach. J Environ Manag. 1997;51(3):229–40. https://doi.org/10.1006/jema.1997.0146.CrossRef

44.

D-h O. A global Malmquist-Luenberger productivity index. J Prod Anal. 2010;34(3):183–97. https://doi.org/10.1007/s11123-010-0178-y.CrossRef

45.

Liu WL, Xia Y, Hou JL. Health expenditure efficiency in rural China using the super-SBM model and the Malmquist productivity index. Int J Equity Health. 2019;18:111. https://doi.org/10.1186/s12939-019-1003-5.CrossRefPubMedPubMedCentral

46.

Li H, Dong S, Liu T. Relative efficiency and productivity: a preliminary exploration of public hospitals in Beijing, China. BMC Health Serv Res. 2014;14(1):158. https://doi.org/10.1186/1472-6963-14-158.CrossRefPubMedPubMedCentral

47.

Sun J, Luo HY. Evaluation on equality and efficiency of health resources allocation and health services utilization in China. Int J Equity Health. 2017;16:127. https://doi.org/10.1186/s12939-017-0614-y.CrossRefPubMedPubMedCentral

48.

Ding JM, Hu XJ, Zhang XZ, Shang L, Yu M, Chen HL. Equity and efficiency of medical service systems at the provincial level of China’s mainland: a comparative study from 2009 to 2014. BMC Public Health. 2018;18:204. https://doi.org/10.1186/s12889-018-5084-7.CrossRef

49.

Leng Y, Liu WW, Xiao NZ, Li YN, Deng J. The impact of policy on the intangible service efficiency of the primary health care institution-based on China’s health care reform policy in 2009. Int J Equity Health. 2019;18:14. https://doi.org/10.1186/s12939-018-0901-2.CrossRefPubMedPubMedCentral

50.

Wang X, Cui Y, Feng R, Li J, Qian Q, Li M, et al. Efficiency characteristics and change of county hospitals. Chinese J Health Policy. 2015;8(6):13–20. [In Chinese]. https://doi.org/10.3969/j.issn.1674-2982.2015.06.003.CrossRef

Title: Measuring productivity of healthcare services under environmental constraints: evidence from China
Authors: Jinna Yu
Zhen Liu
Tingting Zhang
Assem Abu Hatab
Jing Lan
Publication date: 01-12-2020
Publisher: BioMed Central
Published in: BMC Health Services Research / Issue 1/2020
Electronic ISSN: 1472-6963
DOI: https://doi.org/10.1186/s12913-020-05496-9

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Measuring productivity of healthcare services under environmental constraints: evidence from China

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2020

Access to inpatient palliative care among cancer patients in France: an analysis based on the national cancer cohort

From research to clinical practice: a systematic review of the implementation of psychological interventions for chronic headache in adults

Toward health system strengthening in low- and middle-income countries: insights from mathematical modeling of drug supply chains

Improving the participation of adults with visual and severe or profound intellectual disabilities: a process evaluation of a new intervention

Quantitative evaluation of an outreach case management model of care for urban Aboriginal and Torres Strait Islander adults living with complex chronic disease: a longitudinal study

Identifying and understanding determinants of high healthcare costs for breast cancer: a quantile regression machine learning approach