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.

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.

Objective: To evaluate the efficacy and safety of hybutimibe monotherapy or in combination with atorvastatin in the treatment of primary hypercholesterolemia. Methods: This was a multicenter, randomized, double-blind, double-dummy, parallel-controlled phase Ⅲ clinical trial of patients with untreated primary hypercholesterolemia from 41 centers in China between August 2015 and April 2019. Patients were randomly assigned, at a ratio of 1∶1∶1∶1∶1∶1, to the atorvastatin 10 mg group (group A), hybutimibe 20 mg group (group B), hybutimibe 20 mg plus atorvastatin 10 mg group (group C), hybutimibe 10 mg group (group D), hybutimibe 10 mg plus atorvastatin 10 mg group (group E), and placebo group (group F). After a dietary run-in period for at least 4 weeks, all patients were administered orally once a day according to their groups. The treatment period was 12 weeks after the first dose of the study drug, and efficacy and safety were evaluated at weeks 2, 4, 8, and 12. After the treatment period, patients voluntarily entered the long-term safety evaluation period and continued the assigned treatment (those in group F were randomly assigned to group B or D), with 40 weeks' observation. The primary endpoint was the percent change in low density lipoprotein cholesterol (LDL-C) from baseline at week 12. Secondary endpoints included the percent changes in high density lipoprotein cholesterol (HDL-C), triglyceride (TG), apolipoprotein B (Apo B) at week 12 and changes of the four above-mentioned lipid indicators at weeks 18, 24, 38, and 52. Safety was evaluated during the whole treatment period. Results: Totally, 727 patients were included in the treatment period with a mean age of (55.0±9.3) years old, including 253 males. No statistical differences were observed among the groups in demographics, comorbidities, and baseline blood lipid levels. At week 12, the percent changes in LDL-C were significantly different among groups A to F (all P<0.01). Compared to atorvastatin alone, hybutimibe combined with atorvastatin could further improve LDL-C, TG, and Apo B (all P<0.05). Furthermore, there was no significant difference in percent changes in LDL-C at week 12 between group C and group E (P=0.991 7). During the long-term evaluation period, there were intergroup statistical differences in changes of LDL-C, TG and Apo B at 18, 24, 38, and 52 weeks from baseline among the statins group (group A), hybutimibe group (groups B, D, and F), and combination group (groups C and E) (all P<0.01), with the best effect observed in the combination group. The incidence of adverse events was 64.2% in the statins group, 61.7% in the hybutimibe group, and 71.0% in the combination group during the long-term evaluation period. No treatment-related serious adverse events or adverse events leading to death occurred during the 52-week study period. Conclusions: Hybutimibe combined with atorvastatin showed confirmatory efficacy in patients with untreated primary hypercholesterolemia, which could further enhance the efficacy on the basis of atorvastatin monotherapy, with a good overall safety profile.
Male ; Humans ; Middle Aged ; Atorvastatin/therapeutic use* ; Hydroxymethylglutaryl-CoA Reductase Inhibitors/therapeutic use* ; Hypercholesterolemia/drug therapy* ; Cholesterol, LDL/therapeutic use* ; Anticholesteremic Agents/therapeutic use* ; Treatment Outcome ; Triglycerides ; Apolipoproteins B/therapeutic use* ; Double-Blind Method ; Pyrroles/therapeutic use*

OBJECTIVE: To summarize the clinical and laboratory characteristics of patients with acute myeloid leukemia (AML) with inv(16)/t(16;16) (p13.1;q22), and to analyze the risk factors affecting the prognosis of the patients. METHODS: AML patients with inv(16)/t(16;16) (p13.1;q22) and/or CBFβ-MYH11⁺ admitted to the Department of Hematology, The First Affiliated Hospital of Soochow University from January 1, 2008 to October 30, 2019 were retrospective analyzed, the clinical and laboratory indicators, as well as treatment plans and efficacy evaluations of the patients were all recorded. Furthermore, related factors affecting the overall survival (OS) and event-free survival (EFS) of the patients were analyzed. RESULTS: Among 151 AML patients with inv(16)/t(16;16) (p13.1;q22) and/or CBFβ-MYH11⁺, the percentage of additional chromosomal abnormalities was about 27.8%, and the most common additional chromosomal abnormality was +22 (33/151, 21.8%), followed by +8 (11/151, 7.3%). There were 112 patients with perfect NGS examination, and the result showed the most common accompanying gene mutations were KIT mutation (34/112, 30.4%) and FLT3 mutation (23/112, 20.5%). Univariate analysis showed that factors affecting EFS included: NE≤0.5×10⁹/L (P=0.006) and combined K-RAS mutation (P=0.002); Factors affecting OS included: Age≥50 years old (P<0.001) and NE≤0.5×10⁹/L (P=0.016). Multivariate analysis showed that NE≤0.5×10⁹/L (P=0.019) was the risk factors affecting OS. The proportion of bone marrow eosinophilia (BME)≥10.00% (P=0.029) was the risk factors affecting EFS. CONCLUSION The prognosis for those newly diagnosed AML patients who were of advanced age, the high proportion of bone marrow eosinophils, K-RAS mutations, and agranulocytosis is poor. The treatment plans can be adjusted in the early stage to improve the prognosis of such patients.
Chromosome Inversion ; Humans ; Leukemia, Myeloid, Acute/genetics* ; Middle Aged ; Myosin Heavy Chains/genetics* ; Oncogene Proteins, Fusion ; Prognosis ; Retrospective Studies