The Efficiency of Primary Health Care Institutions in the Counties of Hunan Province, China: Data from 2009 to 2017

Zhong, Kaili; Lv, Chen; Cheng, Sixiang; Hongjun, Chen; Long, Fei

doi:10.3390/ijerph17051781

Cited by 31 publications

(42 citation statements)

References 39 publications

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“…The decline was mainly due to the joint constraints of reduced technical efficiency and technical changes, and the effect of the latter was greater than the former. This is consistent with the conclusion of the study of Hunan by Zhong et al [ 48 ]. In order to solve this problem, some cities in the province should increase the introduction of high-tech medical equipment and high-level health personnel in order to improve the level of medical and health technology, thereby increasing the total factor productivity.…”

Section: Discussionsupporting

confidence: 94%

“…In terms of output indicators, due to the complexity and particularity of the medical and health industry, its output is usually measured by disease cure and improvement of people’s health, but these are difficult to quantify. In relative studies, outpatient and emergency visits, rates of utilization of beds, and the number of admitted (discharged) patients are used as quantitative indicators of the supply of medical services to reflect the output of medical and health resources [ 27 , 28 , 48 ].…”

Section: Methodsmentioning

confidence: 99%

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Research on Urban Medical and Health Services Efficiency and Its Spatial Correlation in China: Based on Panel Data of 13 Cities in Jiangsu Province

Lin

Chen

et al. 2021

Healthcare

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The improvement of the efficiency of medical and health services is of great significance for improving the high-quality and efficient medical and health services system and meeting the increasingly diverse health needs of residents. Based on the panel data of 13 cities in Jiangsu Province, this research analyzed the relative effectiveness of medical and health services from 2015 to 2019 using the super efficiency slack-based measure-data envelopment analysis model, and the Malmquist index method was used to explore the changes in the efficiency of medical and health services from a dynamic perspective. Furthermore, the spatial autocorrelation analysis method was used to verify the spatial correlation of medical and health services efficiency. In general, there is room for improvement in the efficiency of medical and health services in 13 cities in Jiangsu Province. There are obvious differences in regional efficiency, and there is a certain spatial correlation. In the future, the medical and health services efficiency of China’s cities should be improved by increasing the investment in high-quality medical and health resources, optimizing their layout and making full use of the spatial spillover effects between neighboring cities to strengthen inter-regional cooperation and exchanges.

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Section: Discussionsupporting

confidence: 94%

Section: Methodsmentioning

confidence: 99%

Research on Urban Medical and Health Services Efficiency and Its Spatial Correlation in China: Based on Panel Data of 13 Cities in Jiangsu Province

Lin

Chen

et al. 2021

Healthcare

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“…In China, the PHC institutions consist of community healthcare centers/stations in urban areas, township hospitals, and village clinics in rural areas [2]. They provide general disease diagnosis, treatment services, and essential public health services [3,4]. After the National Health Reform in 2009, PHC institutions witnessed unprecedented investment from the government [5], reaching to tenfold of the funds in 2018 as compared to that in 2008 [1].…”

Section: Introductionmentioning

confidence: 99%

The trend in primary health care preference in China: a cohort study of 12,508 residents from 2012 to 2018

Wan

Wei

Yin

et al. 2021

BMC Health Serv Res

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Background Residents’ preference for primary health care (PHC) determined their utilization of PHC. This study aimed to assess the determinants of PHC service preference among the residents and the trend in PHC service preference over time in China. Methods We employed the nationally representative longitudinal data from 2012 to 2018 based on the China Family Panel Studies. The analysis framework was guided by the Andersen model of health service utilization. We included a total of 12,508 individuals who have been successfully followed up in the surveys of 2012, 2014, 2016, and 2018 without any missing data. Logistic regressions were performed to analyze potential predictors of PHC preference behavior. Results The results indicated that individuals’ socio-economic circumstances and their health status factors were statistically significant determinants of PHC preference. Notably, over time, the residents’ likelihood of choosing PHC service represented a decreasing trend. Compare to 2012, the likelihood of PHC service preference decreased by 18.6% (OR, 0.814; 95% CI, 0.764–0.867) in 2014, 30.0% (OR, 0.700; 95% CI, 0.657–0.745) in 2016, and 34.9% (OR, 0.651; 95% CI, 0.611–0.694) in 2018. The decrease was significantly associated with the changes in residents’ health status. Conclusions The residents’ likelihood of choosing PHC service represented a decreasing trend, which was contrary to the objective of China’s National Health Reform in 2009. We recommend that policymakers adjust the primary service items in PHC facilities and strengthen the coordination of service between PHC institutions and higher-level hospitals.

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“…Such index systems involve three-dimensional scales of economic, social, and ecological environments, with resource and environment factors mainly used as inputs and economic value or regional output used as expected outputs [6,7]. Meanwhile, in terms of eco-efficiency measurement, current methods include energy analysis [8,9], the index system method [10], material flow analysis [11,12], the ecological footprint method [13,14], and data envelopment analysis [15][16][17][18]. In addition, studies on different objects at different levels of eco-efficiency have covered products [19,20], enterprises [21,22], industries [23,24], and regions [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Measuring Regional Eco-Efficiency in China (2003–2016): A “Full World” Perspective and Network Data Envelopment Analysis

Ren

Zhang

Wang

et al. 2020

IJERPH

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Eco-efficiency enhancement is an inherent requirement of green development and an important indicator of high-quality development in general. It aims to achieve the coordinated development of nature, the economy, and society. Therefore, eco-efficiency measurements should focus on not only total factor input, but also process analysis. Based on the “full world” model in ecological economic theory, this study constructed a theoretical framework for a composite economic-environmental-social system that reflects human welfare and sustainability. To this end, using network data envelopment analysis (DEA), this study established a staged eco-efficiency evaluation model that uses economic, environmental, and social factors to measure the overall and staged eco-efficiency of China’s provinces from 2003 to 2016 and analyze its spatiotemporal characteristics. A geographically weighted regression (GWR) model was also used to analyze the influencing factors of eco-efficiency changes and the spatial differentiation in their effect intensity. The findings were as follows: (1) China’s overall eco-efficiency is still at a low level. It varies significantly from region to region, and only three regions are at the frontier of production. The eastern region has the highest eco-efficiency, followed by the central region, and the gap between the central and western regions has gradually narrowed. In terms of staged efficiency, the level of eco-efficiency in the production stage is less than in the environmental governance stage, which is less than that in the social input stage. (2) In terms of the efficiency of each stage, the efficiency level of the production stage showed a downward trend throughout the entire process, and the decline in the central and western regions was more obvious. The social input stage and the environmental governance stage both showed upward trends. The social input stage showed a higher level, and the increase was relatively flat during the period of study. Efficiency continued to rise during the environmental governance stage from 2003 to 2010 and rose overall, but with some fluctuations from 2011 to 2016. (3) Geographically weighted regression showed that the effects of the influencing factors on eco-efficiency had obvious spatial heterogeneity. The factors affecting overall, production stage, and social input eco-efficiency were, in order of effect intensity from high to low, economic growth level, marketization level, and social input level. In terms of environmental governance, social input level had the greatest impact, followed by economic growth; marketization level did not show a significant impact.

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The Efficiency of Primary Health Care Institutions in the Counties of Hunan Province, China: Data from 2009 to 2017

Cited by 31 publications

References 39 publications

Research on Urban Medical and Health Services Efficiency and Its Spatial Correlation in China: Based on Panel Data of 13 Cities in Jiangsu Province

Research on Urban Medical and Health Services Efficiency and Its Spatial Correlation in China: Based on Panel Data of 13 Cities in Jiangsu Province

The trend in primary health care preference in China: a cohort study of 12,508 residents from 2012 to 2018

Measuring Regional Eco-Efficiency in China (2003–2016): A “Full World” Perspective and Network Data Envelopment Analysis

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