1.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.
2.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.
3.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.
4.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.
5.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.
6.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.
7.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.
8.The Spatial Differences and Dynamic Evolution of China's Healthcare Service Efficiency from 2012 to 2021
Sha-Sha SONG ; Lina SHAO ; Zhonghua SUO ; Jing WU ; Ying LANG
Chinese Health Economics 2024;43(9):70-74,96
Objective:To study the longitudinal trends and spatial clustering characteristics of healthcare service efficiency in China and in North,Northeast,East,Central,South,Southwest,and Northwest China.Methods:The Malmquist index model is used to measure China's healthcare service efficiency from 2012 to 2021,the Dagum Gini coefficient as well as the decomposition method are used to measure the magnitude and source of regional gaps in healthcare service efficiency,and the Kernel density estimation is used to study the longitudinal trend of change and spatial agglomeration characteristics of China's healthcare service efficiency.Results:China's overall healthcare service efficiency is growing,and the inter-regional gap is gradually narrowing,characterized by a concentration trend;the gap in the level of healthcare service efficiency between regions did not widen during the period under examination,but it was found that the gap within some regions was still significant.Conclusion:The national health service efficiency is growing slightly,and the regional gap is generally decreasing,but the Gini coefficient shows that the inter-regional contribution is still the main source of the gap.National health service efficiency is generally concentrated,but some regions are less efficient,with significant internal disparities.
9.Using low-coverage whole genome sequencing technique to analyze the chromosomal copy number alterations in the exfoliative cells of cervical cancer.
Tong REN ; Jing SUO ; Shikai LIU ; Shu WANG ; Shan SHU ; Yang XIANG ; Jing He LANG
Journal of Gynecologic Oncology 2018;29(5):e78-
OBJECTIVES: We analyzed the chromosomal-arm-level copy number alterations (CNAs) in the cervical exfoliative cell and tissue samples by using the low-coverage whole genomic sequencing technique. METHODS: In this study, we retrospectively collected 55 archived exfoliated cervical cell suspension samples and the corresponding formalin-fixed and paraffin-embedded tissue section samples including 27 invasive cervical cancer and 28 control cases. We also collected 19 samples of the cervical exfoliative cells randomly from women to verify the new algorithm model. We analyzed the CNAs in cervical exfoliated cell and tissue samples by using the low-coverage next generation of sequencing. RESULTS: In the model-building study, multiple chromosomal-arm-level CNAs were detected in both cervical exfoliated cell and tissue samples of all cervical cancer cases. By analyzing the consistency of CNAs between exfoliated cells and cervical tissue samples, as well as the heterogeneity in individual patient, we also established a C-score algorithm model according to the chromosomal-arm-level changes of 1q, 2q, 3p, 7q. The C-score model was then validated by the pathological diagnosis of all 74 exfoliated cell samples (including 55 cases in model-building group and 19 cases in verification group). In our result, a cutoff value of C-score > 6 showed 100% sensitivity and 100% specificity in the diagnosis of cervical cancer. CONCLUSION: In this study, we found that CNAs of cervical exfoliated cell samples could robustly distinguish invasive cervical cancer from cancer-free tissues. And we have also developed a C-score algorithm model to process the sequencing data in a more standardized and automated way.
Diagnosis
;
DNA Copy Number Variations
;
Female
;
Genome*
;
High-Throughput Nucleotide Sequencing
;
Humans
;
Mass Screening
;
Population Characteristics
;
Retrospective Studies
;
Sensitivity and Specificity
;
Uterine Cervical Neoplasms*
10.Correlation analysis between meteorological factors, biomass, and active components of Salvia miltiorrhiza in different climatic zones.
Chen-lu ZHANG ; Zong-suo LIANG ; Hong-bo GUO ; Jing-ling LIU ; Yan LIU ; Feng-hua LIU ; Lang-zhu WEI
China Journal of Chinese Materia Medica 2015;40(4):607-613
In this study, the growth and accumulation of active components of Salvia miltiorrhiza in twenty two experimental sites which crossing through three typical climate zones. The S. miltiorrhiza seedlings with the same genotype were planted in each site in spring, which were cultivated in fields with uniform management during their growing seasons till to harvest. The diterpene ketones (dihydrotanshinone, cryptotanshinone, tanshinone I and tanshinone II(A)) in S. miltiorrhiza root samples were determined by using high-performance liquid chromatography (HPLC) method. The biomass of root (root length, number of root branches, root width and dry weight) was also measured. The results showed that tanshinone II(A) in all samples of each site were higher than the standards required by China Pharmacopoeia. It has been found there is a relationship between root shape and climate change. The correlation analysis between active components and meteorological factors showed that the accumulation of tanshinones were effected by such meteorological factors as average relative humidity from April to October > average vapor pressure from April to October > average temperature difference day and night from April to October > annual average temperature and so on. The correlation analysis between root biomass and meteorological factors exhibited that root shape and accumulation of dry matter were affected by those factors, such as average annual aboveground (0-20 cm) temperature from April to October > annual average temperature > average vapor pressure from April to October > annual active accumulated temperature > annual average temperature > average vapor pressure from April to October. The accumulation of tanshinones and biomass was increased with the decrease of latitude. At the same time, the dry matter and diameter of root decreased if altitude rises. In addition, S. miltiorrhiza required sunlight is not sophisticated, when compared with humid and temperature. To sum up, S. miltiorrhiza can adapt to a variety of climatic conditions and the southern warm humid climate is more conducive to its growth and accumulation of active components.
Biomass
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China
;
Climate Change
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Drugs, Chinese Herbal
;
analysis
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Ecosystem
;
Plant Roots
;
chemistry
;
growth & development
;
Salvia miltiorrhiza
;
chemistry
;
growth & development
;
Temperature

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