scholarly journals Equity and Efficiency of Health Resource Allocation of Chinese Medicine in Mainland China: 2013–2017

2020 ◽  
Vol 8 ◽  
Author(s):  
Zhengjun Li ◽  
Lili Yang ◽  
Shaoliang Tang ◽  
Yaoyao Bian
Keyword(s):  
Resource Allocation ◽  
Chinese Medicine ◽  
Mainland China ◽  
Geographic Region ◽  
Health Resource ◽  
Middle Region ◽  
Health Resource Allocation ◽  
Equity And Efficiency ◽  
Statistical Yearbook ◽  
Better Than

Background: In this study, we aimed to estimate the equity and efficiency of traditional Chinese medicine (TCM) health resource allocation, utilization, and trend in mainland China from 2013 to 2017.Methods: The data were downloaded from the China Health Statistical Yearbook (2014–2018) and the China Statistical Yearbook (2018). The equity of TCM health resource allocation was evaluated through the Lorenz curve, Gini coefficient (G), and Theil index (T) based on population size and geographical area. The efficiency and productivity of TCM health resource utilization were assessed using the data envelopment analysis-based Malmquist productivity index.Results: TCM health resource had an increasing trend every year. The equity allocated by population (G ranging from 0.1 to 0.3) was better than that by geographic region (G > 0.5). T in the intra-groups was higher than those in the inter-groups. The equity of TCM resource allocation was the middle region > eastern region > western region. Most provinces (29 out of 31) had negative productivity changes, suggesting deterioration in productivity. Moreover, the middle region with higher scale sizes had more redundant inputs than the other two regions. However, the low technological development (all technical values <1) might hinder productive progress.Conclusion: The equity of TCM health allocated by the population was better than that by the geographic region. The intra-regional difference was the main reason for inequity sources. Productivities in more than 97% of provinces are inefficient. The frequency distribution of scale efficiency (score > 1) had increased since 2015. However, the frequency distribution of technical change (score > 1) decreased every year. The slow technological progress and low scale size might be the main reasons for low productivity.

scholarly journals Equity and efficiency of health resource allocation of Chinese Medicine in mainland China: 2013 to 2017

2020 ◽  
Author(s):  
Zhengjun Li ◽  
Lili Yang ◽  
Yaoyao Bian
Keyword(s):  
Resource Allocation ◽  
Chinese Medicine ◽  
Mainland China ◽  
Health Resource ◽  
Middle Region ◽  
Health Resource Allocation ◽  
Equity And Efficiency ◽  
Statistical Yearbook ◽  
Efficiency And Productivity ◽  
Better Than

Abstract Background: In this study, we aimed to measure the equity, efficiency and productivity of traditional Chinese medicine (TCM) health resource allocation and utilization in mainland China trend from 2013 to 2017. Methods: The data were download from the China Health Statistical Yearbook (2018) and the China Statistical Yearbook (2018). The equity and efficiency of TCM health resource allocation was evaluated by Lorenz curve, Gini coefficient (G) and Theil index (T) were applied to evaluate. The efficiency and productivity of TCM health resource utilization were assessed by Data Envelopment Analysis (DEA)-based Malmquist productivity index (MPI). Results: The TCM health resource had an increasing trend every year. The equity allocated by population (Gs range from 0.1 to 0.3) was better than that by geographic region (Gs more than 0.5). Ts in the intra-groups were higher than that in the inter-groups. Most provinces (29 out of 31) had negative productivity changes, which suggested a deterioration in productivity. However, the middle region with higher scale sizes had more redundant inputs. Moreover, the low technological development (all technical values lower than 1.00) might obstacle the productive progress. Conclusion: The equity of TCM health allocated by population was better than that by the geography region. The intra-regional difference was the main reasons of the sources of inequity. The equity of TCM resource allocation was middle region ＞ eastern region ＞ western region. The productivity in more than 97% provinces are inefficient. The frequency distribution of Sech ( score ＞ 1) increased since 2015. However, the frequency distribution of Techch (score ＞ 1) decreased year by year. The slow technological progress and low scale size might the major reason for the low productivity. Keywords: TCM health resource, Equity, Efficiency, Productivity

scholarly journals Equity and efficiency of health resource allocation of Chinese Medicine in mainland China: 2013 to 2017

2020 ◽  
Author(s):  
Zhengjun Li ◽  
Lili Yang ◽  
Yaoyao Bian
Keyword(s):  
Resource Allocation ◽  
Chinese Medicine ◽  
Mainland China ◽  
Health Resource ◽  
Middle Region ◽  
Health Resource Allocation ◽  
Equity And Efficiency ◽  
Statistical Yearbook ◽  
Efficiency And Productivity ◽  
Better Than

Abstract Background: In this study, we aimed to measure the equity, efficiency and productivity of traditional Chinese medicine (TCM) health resource allocation and utilization in mainland China trend from 2013 to 2017. Methods: The data were download from the China Health Statistical Yearbook (2018) and the China Statistical Yearbook (2018). The equity and efficiency of TCM health resource allocation was evaluated by Lorenz curve, Gini coefficient (G) and Theil index (T) were applied to evaluate. The efficiency and productivity of TCM health resource utilization were assessed by Data Envelopment Analysis (DEA)-based Malmquist productivity index (MPI). Results: The TCM health resource had an increasing trend every year. The equity allocated by population (Gs range from 0.1 to 0.3) was better than that by geographic region (Gs more than 0.5). Ts in the intra-groups were higher than that in the inter-groups. Most provinces (29 out of 31) had negative productivity changes, which suggested a deterioration in productivity. However, the middle region with higher scale sizes had more redundant inputs. Moreover, the low technological development (all technical values lower than 1.00) might obstacle the productive progress. Conclusion: The equity of TCM health allocated by population was better than that by the geography region. The intra-regional difference was the main reasons of the sources of inequity. The equity of TCM resource allocation was middle region ＞ eastern region ＞ western region. The productivity in more than 97% provinces are inefficient. The frequency distribution of Sech ( score ＞ 1) increased since 2015. However, the frequency distribution of Techch (score ＞ 1) decreased year by year. The slow technological progress and low scale size might the major reason for the low productivity. Keywords: TCM health resource, Equity, Efficiency, Productivity

scholarly journals Equity and efficiency of health care resource allocation in Jiangsu Province, China

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Qian Li ◽  
Jianjun Wei ◽  
Fengchang Jiang ◽  
Guixiang Zhou ◽  
Rilei Jiang ◽  
...  
Keyword(s):  
Health Care ◽  
Resource Allocation ◽  
Geographical Area ◽  
Health Care Resource ◽  
Jiangsu Province ◽  
Financial Resources ◽  
Health Resource ◽  
Statistical Yearbook ◽  
Efficiency And Productivity ◽  
Better Than

Abstract Background Jiangsu was one of the first four pilot provinces to engage in comprehensive health care reform in China, which has been on-going for the past 5 years. This study aims to evaluate the equity, efficiency and productivity of health care resource allocation in Jiangsu Province using the most recent data, analyse the causes of deficiencies, and discuss measures to solve these problems. Methods Data were extracted from the Jiangsu Health/Family Planning Statistical Yearbook (2015–2019) and Jiangsu Statistical Yearbook (2015–2019). The Gini coefficient (G), Theil index (T) and health resource density index (HRDI) were chosen to study the fairness of health resource allocation in Jiangsu Province. Data envelopment analysis (DEA) and the Malmquist productivity index (MPI) were used to analyse the efficiency and productivity of this allocation. Results From 2014 to 2018, the total amount of health resources in Jiangsu Province increased. The G of primary resource allocation by population remained below 0.15, and that by geographical area was between 0.14 and 0.28; additionally, the G of health financial resources was below 0.26, and that by geographical area was above 0.39. T was consistent with the results for G and Lorenz curves. The HRDI shows that the allocated amounts of health care resources were the highest in southern Jiangsu, except for the number of health institutions. The average value of TE was above 0.93, and the DEA results were invalid for only two cities. From 2014 to 2018, the mean TFPC in Jiangsu was less than 1, and the values exceeded 1 for only five cities. Conclusion The equity of basic medical resources was better than that of financial resources, and the equity of geographical allocation was better than that of population allocation. The overall efficiency of health care resource allocation was high; however, the total factor productivity of the whole province has declined due to technological regression. Jiangsu Province needs to further optimize the allocation and increase the utilization efficiency of health care resources.

scholarly journals Equity and Efficiency of Medical and Health Resource Allocation in Western China Based on Health Resources Agglomeration Degree and Malmquist Index

2021 ◽  
Author(s):  
Ju Qiu ◽  
Ruixia Yan
Keyword(s):  
Resource Allocation ◽  
Malmquist Index ◽  
Health Resources ◽  
Western China ◽  
Resources Allocation ◽  
Health Resource ◽  
Factor Productivity ◽  
Health Resource Allocation ◽  
Social And Economic Development ◽  
Equity And Efficiency

Abstract Background: There is unequal distribution and low utilization efficiency of medical and health resources among regions in China for regional inequality social and economic development. The social and economic development in western China is relatively backward nationwide. The quantity and quality of medical and health resources have become a social problem in western China. This study aims to evaluate the equity and efficiency of health resource allocation in western China. Methods: The research data are extracted from China Statistical Yearbook ( 2009 – 2019 ). We choose the health resource agglomeration degree ( HRAD ) theory and Malmquist index to evaluate the equity and efficiency of health resource allocation in western China. The HRAD shows the impact of demographic and geographic factors on health resource aggregation. Results: From the latest data in 2019, the equity of medical and health resources allocation based on geographical area in Inner Mongolia, Tibet, Qinghai and Xinjiang is poor. The equity of medical and health resources allocation based on population allocation in Yunnan, Tibet, Gansu, Qinghai, Ningxia and Xinjiang is poor. From 2009 to 2019, the equity of health resource allocation in western China are poor nationwide. The total factor productivity of Tibet, Qinghai, Ningxia and Xinjiang was less than 1, which were 0.994, 0.984, 0.974 and 0.994, respectively. The allocation of medical and health resources in western China is unfair. Conclusion: The government should pay attention to the horizontal flow of medical and health resources when investing in medical and health resources. Population and geographical factors should be taken into account when allocating health resources. The total factor productivity in the western region is greatly affected by the technological progress rate. We should increase investment in medical and health technology, and effectively optimize the equity and efficiency of medical and health resource allocation.

scholarly journals Differences in regional distribution and inequality in health-resource allocation at hospital and primary health centre levels: a longitudinal study in Shanghai, China

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e035635
Author(s):  
Enhong Dong ◽  
Shipeng Liu ◽  
Minjie Chen ◽  
Hongmei Wang ◽  
Li-Wu Chen ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Health Centre ◽  
Regional Distribution ◽  
Primary Health Centre ◽  
Theil Index ◽  
Health Resource ◽  
Healthcare Resources ◽  
Health Resource Allocation ◽  
Primary Health ◽  
Inequality In Health

ObjectivesTo analyse differences in regional distribution and inequality in health-resource allocation at the hospital and primary health centre (PHC) levels in Shanghai over 7 years.DesignA longitudinal survey using 2010–2016 data, which were collected for analysis.SettingThe study was conducted at the hospital and PHC levels in Shanghai, China.Outcome measuresTen health-resource indicators were used to measure health-resource distribution at the hospital and PHC levels. In addition, the Theil Index was calculated to measure inequality in health-resource allocation.ResultsAll quantities of healthcare resources per 1000 people in hospitals and PHCs increased across Shanghai districts from 2010 to 2016. Relative to suburban districts, the central districts had higher ratios, both in terms of doctors and equipment, and had faster growth in the doctor indicator and slower growth in the equipment indicator in hospitals and PHCs. The Theil Indices of all health-resource allocation in hospitals had higher values compared with those in PHCs every year from 2010 to 2016; furthermore, the Theil Indices of the indicators, except for technicians and doctors in hospitals, all exhibited downward time trends in hospitals and PHCs.ConclusionsIncreased healthcare resources and reduced inequality of health-resource allocation in Shanghai during the 7 years indicated that measures taken by the Shanghai government to deepen the new round of healthcare reform in China since 2009 had been successful. Meanwhile there still existed regional difference between urban and rural areas and inequality across different medical institutions. To solve these problems, we prescribe increased wages, improved working conditions, and more open access to career development for doctors and nurses; reduced investments in redundant equipment in hospitals; and other incentives for balancing the health workforce between hospitals and PHCs.

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