Evaluating the efficiency of primary health care institutions in China: an improved three-stage data envelopment analysis approach
Abstract Background Primary health care (PHC) institutions are key to realizing the main functions of the health care system. Since the new health care reform in 2009, the Chinese government has invested heavily in PHC institutions and launched favorable initiatives to improve the efficiency of such...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-09-01
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| Series: | BMC Health Services Research |
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| Online Access: | https://doi.org/10.1186/s12913-023-09979-3 |
| _version_ | 1797577274408566784 |
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| author | Wanmin Su Yatian Hou Mengge Huang Jiamian Xu Qingfeng Du Peixi Wang |
| author_facet | Wanmin Su Yatian Hou Mengge Huang Jiamian Xu Qingfeng Du Peixi Wang |
| author_sort | Wanmin Su |
| collection | DOAJ |
| description | Abstract Background Primary health care (PHC) institutions are key to realizing the main functions of the health care system. Since the new health care reform in 2009, the Chinese government has invested heavily in PHC institutions and launched favorable initiatives to improve the efficiency of such institutions. This study is designed to gauge the efficiency of PHC institutions by using 2012–2020 panel data covering 31 provinces in China. Methods This study applied an improved three-stage data envelopment analysis (DEA) model to evaluate the efficiency of PHC institutions in China. Unlike the traditional three-stage DEA model, the input-oriented global super-efficiency slack-based measurement (SBM) DEA model is used to calculate the efficiency in the first and third stages of the improved three-stage DEA model, which not only allows the effects of environmental factors and random noise to be taken into account but also deal with the problem of slack, super-efficiency and the comparability of interperiod efficiency values throughout the efficiency measurement. Results The results show that the efficiency of PHC institutions has been overestimated due to the impact of external environmental factors and random noise. From 2012 to 2020, the efficiency of PHC institutions displayed a downward trend. Moreover, there are significant differences in the efficiency of PHC institutions between regions, with the lowest efficiency being found in the northeast region. The efficiency of PHC institutions is significantly affected by residents’ annual average income, per capita GDP, population density, the percentage of the population aged 0–14, the percentage of the population aged 65 and older, the number of people with a college education and above per 100,000 residents, and the proportion of the urban population. Conclusions Substantial investment in PHC institutions has not led to the expected efficiency gains. Therefore, more effective measures should be taken to improve the efficiency of PHC institutions in China based on local conditions. This study provides a new analytical approach to calculating the efficiency of PHC institutions, and this approach can be applied to efficiency evaluation either in other fields or in other countries. |
| first_indexed | 2024-03-10T22:05:54Z |
| format | Article |
| id | doaj.art-ea4fd29fb7ba4d0c959b73c985b7d546 |
| institution | Directory Open Access Journal |
| issn | 1472-6963 |
| language | English |
| last_indexed | 2024-03-10T22:05:54Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj.art-ea4fd29fb7ba4d0c959b73c985b7d5462023-11-19T12:48:31ZengBMCBMC Health Services Research1472-69632023-09-0123111910.1186/s12913-023-09979-3Evaluating the efficiency of primary health care institutions in China: an improved three-stage data envelopment analysis approachWanmin Su0Yatian Hou1Mengge Huang2Jiamian Xu3Qingfeng Du4Peixi Wang5General Practice Center, The Seventh Affiliated Hospital, Southern Medical UniversitySchool of Nursing and Health, Henan UniversitySchool of Nursing and Health, Henan UniversityGeneral Practice Center, The Seventh Affiliated Hospital, Southern Medical UniversityGeneral Practice Center, The Seventh Affiliated Hospital, Southern Medical UniversityGeneral Practice Center, The Seventh Affiliated Hospital, Southern Medical UniversityAbstract Background Primary health care (PHC) institutions are key to realizing the main functions of the health care system. Since the new health care reform in 2009, the Chinese government has invested heavily in PHC institutions and launched favorable initiatives to improve the efficiency of such institutions. This study is designed to gauge the efficiency of PHC institutions by using 2012–2020 panel data covering 31 provinces in China. Methods This study applied an improved three-stage data envelopment analysis (DEA) model to evaluate the efficiency of PHC institutions in China. Unlike the traditional three-stage DEA model, the input-oriented global super-efficiency slack-based measurement (SBM) DEA model is used to calculate the efficiency in the first and third stages of the improved three-stage DEA model, which not only allows the effects of environmental factors and random noise to be taken into account but also deal with the problem of slack, super-efficiency and the comparability of interperiod efficiency values throughout the efficiency measurement. Results The results show that the efficiency of PHC institutions has been overestimated due to the impact of external environmental factors and random noise. From 2012 to 2020, the efficiency of PHC institutions displayed a downward trend. Moreover, there are significant differences in the efficiency of PHC institutions between regions, with the lowest efficiency being found in the northeast region. The efficiency of PHC institutions is significantly affected by residents’ annual average income, per capita GDP, population density, the percentage of the population aged 0–14, the percentage of the population aged 65 and older, the number of people with a college education and above per 100,000 residents, and the proportion of the urban population. Conclusions Substantial investment in PHC institutions has not led to the expected efficiency gains. Therefore, more effective measures should be taken to improve the efficiency of PHC institutions in China based on local conditions. This study provides a new analytical approach to calculating the efficiency of PHC institutions, and this approach can be applied to efficiency evaluation either in other fields or in other countries.https://doi.org/10.1186/s12913-023-09979-3Primary health care institutionsEfficiency measurementSuper-efficiency SBM DEA modelThree-stage DEA modelGlobal benchmarking techniqueExternal environment factors |
| spellingShingle | Wanmin Su Yatian Hou Mengge Huang Jiamian Xu Qingfeng Du Peixi Wang Evaluating the efficiency of primary health care institutions in China: an improved three-stage data envelopment analysis approach BMC Health Services Research Primary health care institutions Efficiency measurement Super-efficiency SBM DEA model Three-stage DEA model Global benchmarking technique External environment factors |
| title | Evaluating the efficiency of primary health care institutions in China: an improved three-stage data envelopment analysis approach |
| title_full | Evaluating the efficiency of primary health care institutions in China: an improved three-stage data envelopment analysis approach |
| title_fullStr | Evaluating the efficiency of primary health care institutions in China: an improved three-stage data envelopment analysis approach |
| title_full_unstemmed | Evaluating the efficiency of primary health care institutions in China: an improved three-stage data envelopment analysis approach |
| title_short | Evaluating the efficiency of primary health care institutions in China: an improved three-stage data envelopment analysis approach |
| title_sort | evaluating the efficiency of primary health care institutions in china an improved three stage data envelopment analysis approach |
| topic | Primary health care institutions Efficiency measurement Super-efficiency SBM DEA model Three-stage DEA model Global benchmarking technique External environment factors |
| url | https://doi.org/10.1186/s12913-023-09979-3 |
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