Objective:To review the health service system,personnel salary system and service model in Macao Special Administrative Region(Macao),and analyze the reference significance for the of the tightly-knit medical alliance construction.Methods:On-site visits were conducted to 2 hospitals,3 community health service centers and 1 nursing home in Macao.Interviews and focus group discussions were held with key informants.Literature review was carried out at the same time.Qualitative and quantitative analysis methods were used to analyze the collected data.Results:Macao has clearly defined the functional positioning of public hospitals and primary healthcare institutions,and has given full play to the supplementary role of private medical institutions,establishing a grid-based medical and health service system with clear division of labor and public-private cooperation.Macao has implemented a strict system of first diagnosis in primary health care and two-way referral,providing high-quality and efficient medical and health services to community residents.Macao has institutionalized the salary system for medical personnel,with only a small income gap between different types of institutions and personnel,establishing a homogenized high-level medical and health salary system.Conclusion:The mainland should strengthen the financial protection,and promote the coordinated management of staff,funds and properties.Institutionalize the income and responsibility system for medical personnel,and narrow the income gap between different types of institutions and personnel,strengthen the functional positioning of primary health care and services for key populations,and promote first diagnosis in primary health care and two-way referral.

Objective:To sort out the situation,content and practical experience of the"four sinkings"in the construction of tightly-knit county medical communities,and provide references and basis for the promotion of policies in various regions.Methods:Quantitative description methods were used to analyze the development trends of the number of health technicians and medical services in primary healthcare facilities.Literature research methods were used to analyze policy content.Case analysis methods were used to summarize and analyze the implementation content and support mechanisms of the"four sinkings"in Youxi County of Fujian Province,Dongyang City of Zhejiang Province,Dongtai City of Jiangsu Province and Suixi County of Anhui Province.Results:From 2010-2022,more health technicians and medical services still flew to hospitals,indicating that personnel is an important impact factor for service sinking.The task of personnel and service sinking to the primary healthcare level is still arduous.From 2023 to 2024,China has issued multiple national policies to clarify the content of the"four sinkings",among which personnel sinking is the core,technology sinking is the means,management sinking is the support,and service sinking is the goal.Case analysis result shows that typical regions have established a long-term mechanism to promote the"four sinkings"through talent pool constrution,economic incentives,medical insurance support and informatization construction.Conclusion and Suggestions:It is needed to enhance the understanding of the"four sinkings",and promote them as important policy measures;promote the experience of typical regions,with a focus on the establishment of a long-term mechanism;and strengthen trace study and monitoring and evaluation to ensure the implementation of the"four sinkings".

Objective:To investigate the correlation between peri-coronary fat attenuation index (FAI) and plaque formation in patients with myocardial bridge (MB) of the left anterior descending artery (LAD) using coronary computed tomography angiography (CCTA) and to develop an optimal predictive model to explore the potential application of FAI in the primary prevention of MB related atherosclerosis.Methods:In this retrospective study, prediction models associated with perivascular fat inflammation were developed and validated using both logistic regression and machine learning (ML) algorithm. A training dataset was collected from 253 patients who underwent ≥2 coronary computed tomography angiography (CCTA) with ≥3 months intervals from one tertiary hospital from January 2007 to April 2021 and had baseline CCTA showing no plaques in LAD MB. The median follow-up time was 3.2 years. According to the same criteria, a total of 75 LAD MB patients from four other hospitals were included to form an independent external validation dataset, with a median follow-up time of 1.8 years. Receiver operating characteristic (ROC) curve analysis with integrated discrimination improvement (IDI) and category net reclassification index (NRI) were used to compare the performance of the predictive models.Results:62 patients (24.5%) in the training dataset had proximal plaque formation in LAD MB, while 22 patients (29.3%) in the external validation dataset had plaque formation during the follow-up period. Baseline FAI within the longitudinal distance equal to 30 mm proximal to the MB entrance was an independent predictor ( OR=1.068, P=0.046). According to the model results, ROC curves were plotted. The AUC of Model 1 was 0.822, and the AUCs of Model 2 and 1 were 0.821 and 0.591 in the training dataset. After the DeLong test, the AUC of Model 1 was superior to that of Model 2 ( Z=2.839, P=0.005) and Model 1 ( Z=6.124, P<0.001). These findings were further validated in the external validation dataset, where ML-model 3 yielded the best predictive performance, outperforming the logistic regression-based Model 2 (categorical NRI=0.359, P=0.048; IDI=0.108, P=0.046). Conclusion:FAI measured within the 30 mm proximal to the entrance of MBs due to its prone to plaque development is an independent predictor for atherosclerotic plaque formation. The ML-prediction model based on a decision tree algorithm combines FAI, MB anatomical features, and patient risk factors, which is beneficial for patients undergoing routine CCTA examination to identify inflamed coronary arteries in advance and guide the clinical adoption of more targeted preventive treatment, including anti-inflammatory treatment.

Objective:To investigate the correlation between peri-coronary fat attenuation index (FAI) and plaque formation in patients with myocardial bridge (MB) of the left anterior descending artery (LAD) using coronary computed tomography angiography (CCTA) and to develop an optimal predictive model to explore the potential application of FAI in the primary prevention of MB related atherosclerosis.Methods:In this retrospective study, prediction models associated with perivascular fat inflammation were developed and validated using both logistic regression and machine learning (ML) algorithm. A training dataset was collected from 253 patients who underwent ≥2 coronary computed tomography angiography (CCTA) with ≥3 months intervals from one tertiary hospital from January 2007 to April 2021 and had baseline CCTA showing no plaques in LAD MB. The median follow-up time was 3.2 years. According to the same criteria, a total of 75 LAD MB patients from four other hospitals were included to form an independent external validation dataset, with a median follow-up time of 1.8 years. Receiver operating characteristic (ROC) curve analysis with integrated discrimination improvement (IDI) and category net reclassification index (NRI) were used to compare the performance of the predictive models.Results:62 patients (24.5%) in the training dataset had proximal plaque formation in LAD MB, while 22 patients (29.3%) in the external validation dataset had plaque formation during the follow-up period. Baseline FAI within the longitudinal distance equal to 30 mm proximal to the MB entrance was an independent predictor ( OR=1.068, P=0.046). According to the model results, ROC curves were plotted. The AUC of Model 1 was 0.822, and the AUCs of Model 2 and 1 were 0.821 and 0.591 in the training dataset. After the DeLong test, the AUC of Model 1 was superior to that of Model 2 ( Z=2.839, P=0.005) and Model 1 ( Z=6.124, P<0.001). These findings were further validated in the external validation dataset, where ML-model 3 yielded the best predictive performance, outperforming the logistic regression-based Model 2 (categorical NRI=0.359, P=0.048; IDI=0.108, P=0.046). Conclusion:FAI measured within the 30 mm proximal to the entrance of MBs due to its prone to plaque development is an independent predictor for atherosclerotic plaque formation. The ML-prediction model based on a decision tree algorithm combines FAI, MB anatomical features, and patient risk factors, which is beneficial for patients undergoing routine CCTA examination to identify inflamed coronary arteries in advance and guide the clinical adoption of more targeted preventive treatment, including anti-inflammatory treatment.

Objective:To sort out the situation,content and practical experience of the"four sinkings"in the construction of tightly-knit county medical communities,and provide references and basis for the promotion of policies in various regions.Methods:Quantitative description methods were used to analyze the development trends of the number of health technicians and medical services in primary healthcare facilities.Literature research methods were used to analyze policy content.Case analysis methods were used to summarize and analyze the implementation content and support mechanisms of the"four sinkings"in Youxi County of Fujian Province,Dongyang City of Zhejiang Province,Dongtai City of Jiangsu Province and Suixi County of Anhui Province.Results:From 2010-2022,more health technicians and medical services still flew to hospitals,indicating that personnel is an important impact factor for service sinking.The task of personnel and service sinking to the primary healthcare level is still arduous.From 2023 to 2024,China has issued multiple national policies to clarify the content of the"four sinkings",among which personnel sinking is the core,technology sinking is the means,management sinking is the support,and service sinking is the goal.Case analysis result shows that typical regions have established a long-term mechanism to promote the"four sinkings"through talent pool constrution,economic incentives,medical insurance support and informatization construction.Conclusion and Suggestions:It is needed to enhance the understanding of the"four sinkings",and promote them as important policy measures;promote the experience of typical regions,with a focus on the establishment of a long-term mechanism;and strengthen trace study and monitoring and evaluation to ensure the implementation of the"four sinkings".

Objective:To review the health service system,personnel salary system and service model in Macao Special Administrative Region(Macao),and analyze the reference significance for the of the tightly-knit medical alliance construction.Methods:On-site visits were conducted to 2 hospitals,3 community health service centers and 1 nursing home in Macao.Interviews and focus group discussions were held with key informants.Literature review was carried out at the same time.Qualitative and quantitative analysis methods were used to analyze the collected data.Results:Macao has clearly defined the functional positioning of public hospitals and primary healthcare institutions,and has given full play to the supplementary role of private medical institutions,establishing a grid-based medical and health service system with clear division of labor and public-private cooperation.Macao has implemented a strict system of first diagnosis in primary health care and two-way referral,providing high-quality and efficient medical and health services to community residents.Macao has institutionalized the salary system for medical personnel,with only a small income gap between different types of institutions and personnel,establishing a homogenized high-level medical and health salary system.Conclusion:The mainland should strengthen the financial protection,and promote the coordinated management of staff,funds and properties.Institutionalize the income and responsibility system for medical personnel,and narrow the income gap between different types of institutions and personnel,strengthen the functional positioning of primary health care and services for key populations,and promote first diagnosis in primary health care and two-way referral.

Objective:To assess the impact of health insurance packaged payment in medical communities on the economicburden of patients,income and satisfaction of medical staff in counties.Methods:Using sample data from 2018-2022 from the national monitoring counties of medical communities,taking 2020 as the year of implementation of packaged payment,a double difference model was constructed with county population density and county per capita GDP as the control variables to assess the impact of packaged payment on the economic burden of patients,medical staff income and satisfaction in the county.Results:The packaged payment policy reduced the economic burden of patients to a certain extent and had a statistically significant positive effect on medical staff income in 2021,but it did not significantly increase the satisfaction of both supply and demand.Conclusion:The implementation of health insurance packaged payment of the MEC will not increase the economic burden of patients.It has a good pro-poor effect,and the income of medical staff has been improved to some extent,but there is still room for optimisation and improvement of the policy.

Objective:To assess the impact of health insurance packaged payment in medical communities on the economicburden of patients,income and satisfaction of medical staff in counties.Methods:Using sample data from 2018-2022 from the national monitoring counties of medical communities,taking 2020 as the year of implementation of packaged payment,a double difference model was constructed with county population density and county per capita GDP as the control variables to assess the impact of packaged payment on the economic burden of patients,medical staff income and satisfaction in the county.Results:The packaged payment policy reduced the economic burden of patients to a certain extent and had a statistically significant positive effect on medical staff income in 2021,but it did not significantly increase the satisfaction of both supply and demand.Conclusion:The implementation of health insurance packaged payment of the MEC will not increase the economic burden of patients.It has a good pro-poor effect,and the income of medical staff has been improved to some extent,but there is still room for optimisation and improvement of the policy.

Objective:To assess the impact of health insurance packaged payment in medical communities on the economicburden of patients,income and satisfaction of medical staff in counties.Methods:Using sample data from 2018-2022 from the national monitoring counties of medical communities,taking 2020 as the year of implementation of packaged payment,a double difference model was constructed with county population density and county per capita GDP as the control variables to assess the impact of packaged payment on the economic burden of patients,medical staff income and satisfaction in the county.Results:The packaged payment policy reduced the economic burden of patients to a certain extent and had a statistically significant positive effect on medical staff income in 2021,but it did not significantly increase the satisfaction of both supply and demand.Conclusion:The implementation of health insurance packaged payment of the MEC will not increase the economic burden of patients.It has a good pro-poor effect,and the income of medical staff has been improved to some extent,but there is still room for optimisation and improvement of the policy.

Objective:To assess the impact of health insurance packaged payment in medical communities on the economicburden of patients,income and satisfaction of medical staff in counties.Methods:Using sample data from 2018-2022 from the national monitoring counties of medical communities,taking 2020 as the year of implementation of packaged payment,a double difference model was constructed with county population density and county per capita GDP as the control variables to assess the impact of packaged payment on the economic burden of patients,medical staff income and satisfaction in the county.Results:The packaged payment policy reduced the economic burden of patients to a certain extent and had a statistically significant positive effect on medical staff income in 2021,but it did not significantly increase the satisfaction of both supply and demand.Conclusion:The implementation of health insurance packaged payment of the MEC will not increase the economic burden of patients.It has a good pro-poor effect,and the income of medical staff has been improved to some extent,but there is still room for optimisation and improvement of the policy.